omni optimizer a generic evolutionary algorithm for single and multi objective optimization due to the vagaries of optimization problems encountered in practice users resort to different algorithms for solving different optimization problems in this paper we suggest and evaluate an optimization procedure which specializes in solving a wide variety of optimization problems the proposed algorithm is designed as a generic multi objective multi optima optimizer care has been taken while designing the algorithm such that it automatically degenerates to efficient algorithms for solving other simpler optimization problems such as single objective uni optimal problems single objective multi optima problems and multi objective uni optimal problems the efficacy of the proposed algorithm in solving various problems is demonstrated on a number of test problems chosen from the literature because of its efficiency in handling different types of problems with equal ease this algorithm should find increasing use in real world optimization problems

a hybrid particle swarm optimization with a feasibility based rule for constrained optimization during the past decade hybrid algorithms combining evolutionary computation and constraint handling techniques have shown to be effective to solve constrained optimization problems for constrained optimization the penalty function method has been regarded as one of the most popular constraint handling technique so far whereas its drawback lies in the determination of suitable penalty factors which greatly weakens the efficiency of the method as a novel population based algorithm particle swarm optimization pso has gained wide applications in a variety of fields especially for unconstrained optimization problems in this paper a hybrid pso hpso with a feasibility based rule is proposed to solve constrained optimization problems in contrast to the penalty function method the rule requires no additional parameters and can guide the swarm to the feasible region quickly in addition to avoid the premature convergence simulated annealing sa is applied to the best solution of the swarm to help the algorithm escape from local optima simulation and comparisons based on several well studied benchmarks demonstrate the effectiveness efficiency and robustness of the proposed hpso moreover the effects of several crucial parameters on the performance of the hpso are studied as well

application of particle swarm optimization based on chks smoothing function for solving nonlinear bilevel programming problem particle swarm optimization pso is a new optimization technique originating from artificial life and evolutionary computation it completes optimization through following the personal best solution of each particle and the global best value of the whole swarm pso can be used to solve nonlinear programming problems for global optimal solutions efficiently so a novel approach based on particle swarm optimization is proposed to solve nonlinear bilevel programming problem nblp in the proposed approach applying karush kuhn tucker kkt condition to the lower level problem we transform the nblp into a regular nonlinear programming with complementary constraints which is sequentially smoothed by chen harker kanzow smale chks smoothing function the pso approach is then applied to solve the smoothed nonlinear programming for getting the approximate optimal solution of the nblp problem simulations on 5 benchmark problems and practical example about watershed water trading decision making problem are made and the results demonstrate the effectiveness of the proposed method for solving nblp

genetic algorithm based technique for solving chance constrained problems management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information chance constrained programming ccp have been used for modelling and analysis of risks in a number of application domains however the resulting mathematical problems are non trivial to represent using algebraic modelling languages and pose significant computational challenges due to their non linear non convex and the stochastic nature we develop and implement c classes to represent such ccp problems we propose a framework consisting of genetic algorithm and monte carlo simulation in order to process the problems the non linear and non convex nature of the ccp problems are processed using genetic algorithm whereas the stochastic nature is addressed through simulation the computational investigations have shown that the framework can efficiently represent and obtain good solutions for seven test problems

a local exploration based differential evolution algorithm for constrained global optimization abstract we propose a modified differential evolution de algorithm for constrained global optimization the modification is based on the mutation rule of de the new algorithm also incorporates a periodic local exploration technique the local technique used is a limited version of the pattern search ps method the penalty functions such as the superiority of feasible points sfp and the parameter free penalty pfp are used for handling constraints we numerically study sfp and pfp and based on a drawback observed we adapt the selection rule of de the new algorithm is tested on 45 test problems comparisons are made with some recent algorithms

a novel meta heuristic optimization algorithm inspired by group hunting of animals hunting search a novel optimization algorithm is presented inspired by group hunting of animals such as lions wolves and dolphins although these hunters have differences in the way of hunting they are common in that all of them look for a prey in a group the hunters encircle the prey and gradually tighten the ring of siege until they catch the prey in addition each member of the group corrects its position based on its own position and the position of other members if the prey escapes from the ring hunters reorganize the group to siege the prey again several benchmark numerical optimization problems constrained and unconstrained are presented here to demonstrate the effectiveness and robustness of the proposed hunting search hus algorithm the results indicate that the proposed method is a powerful search and optimization technique it yields better solutions compared to those obtained by some current algorithms when applied to continuous problems

differential evolution with dynamic stochastic selection for constrained optimization how much attention should be paid to the promising infeasible solutions during the evolution process is investigated in this paper stochastic ranking has been demonstrated as an effective technique for constrained optimization in stochastic ranking the comparison probability will affect the position of feasible solution after ranking and the quality of the final solutions in this paper the dynamic stochastic selection dss is put forward within the framework of multimember differential evolution firstly a simple version named dss mde is given where the comparison probability decreases linearly the algorithm dss mde has been compared with two state of the art evolution strategies and three competitive differential evolution algorithms for constrained optimization on 13 common benchmark functions dss mde is also evaluated on four well studied engineering design examples and the experimental results are significantly better than current available results secondly other dynamic settings of the comparison probability for dss mde are also designed and tested from the experimental results dss mde is effective for constrained optimization finally dss mde with a square root adjusted comparison probability is evaluated on the 22 benchmark functions in cec 06 and the experimental results on most functions are competitive

a modified real coded genetic algorithm for constrained optimization abstract the performance of a genetic algorithm ga largely depends upon crossover and mutation operators deep and thakur 2007 14 15 proposed a real coded genetic algorithm rcga incorporating laplace crossover lx and power mutation pm operator and shown that the resulting ga named lx pm outperforms many existing rcgas on a large set of scalable test problems of varying difficulty level in this paper lx pm is modified by improving the lx operator the modified lx operator named as bounded exponential crossover bex operator always creates offspring within the variable bounds a new rcga named bex pm incorporating bex and pm operator is proposed the performance of the modified ga is tested against the original algorithm lx pm and three other popular constrained optimization algorithms hx num hx mptm and sbx pol over a test suite containing twenty five constrained optimization problems collected from global optimization literature the performance of all rcgas and quality of solution obtained is compared on the basis of standard criteria used in ga literature the comparative study shows that bex pm performs significantly better than the original algorithm lx pm and outperforms all rcgas considered in this study

dimensional reduction in constrained global optimization on smooth manifolds display omitted approach to reduce the dimension of search domains in constrained global optimization on smooth manifolds more equality constraints more dimensional reduction mappings under optimization do not need to be differentiable or even continuous optimization task is executed while keeping candidate points populations within feasible submanifolds the proposed paradigm may be employed jointly with an extensive family of already established metaheuristics this work introduces an approach aimed at reducing the dimension of search domains in constrained global optimization of real valued functions defined on smooth manifolds and subject also and mainly to equality constraints the functions expressing the cited constraints must satisfy certain smoothness conditions and other types of restrictions are simultaneously possible but the effect of dimensional reduction will be proportional to the number of equality constraints the objective functions under study do not need to be differentiable or even continuous and it is shown that the optimization task will be executed so that candidate points remain in the corresponding submanifolds evolving there during the whole optimization process the proposed paradigm may be employed jointly with an extensive family of already established metaheuristics after introducing the fundamental ideas and establishing the theoretical basis some examples will illustrate the effectiveness of the proposed method

adaptive memetic differential evolution with global and local neighborhood based mutation operators differential evolution de is one of the most popular optimization methods for real valued problems and a large number of its variants have been proposed so far however bringing together different ideas that already led to successful de versions is rare in the literature in the present paper a novel de algorithm is proposed in which three among the most efficient concepts already applied separately within de framework are gathered together namely 1 the adaptation of algorithm control parameters and probabilities of using different mutation strategies 2 the use of nelder mead algorithm as a local search method hybridized with de and 3 the splitting mutation into global and local models when local mutation model is based on the concept of neighborhood of individuals organized on a ring topology the performance of the novel algorithm called adaptive memetic de with global and local neighborhood based mutation operators is compared with 13 different de variants on a set of 25 popular problems which include rotated shifted and hybrid composition functions it is found that although none de algorithm outperforms all the others for the majority of problems on average the proposed approach perform better than all 13 de algorithms selected for comparison the proposed algorithm is another heuristic approach developed to solve optimization problems the question may arise whether proposing novel methods is useful as no free lunch theorems for optimization state that the expected performance of all possible heuristics on all possible problems is equal in the last section of the paper the limitations and implications of no free lunch theorems are discussed based on rich but unfortunately frequently neglected literature a very simple continuous and differentiable minimization problem is proposed for which it is empirically verified that each among considered 14 de algorithms perform poorer than random sampling it is also empirically shown that when all considered de algorithms search for the maximum of the proposed problem they found lower minimum than de algorithms searching for the minimum such result is not unexpected according to no free lunch theorems and should be considered as a precaution from generalization of good performance of heuristic optimization methods

shrinking hypersphere based trajectory of particles in pso this paper proposes a new shrinking hypersphere pso shpso for continuous function optimization the global best and personal best fly in the search space in the form of a hypersphere instead of particles the hyperspheres keep on shrinking as the iterations proceed and the velocity and position update equations are applied at each iteration the theoretical convergence of the shpso is proved then the performance of the proposed shpso is compared with five well known pso variants namely basic pso trelea i pso trelea ii pso clerc pso and spso 2011 the basis of comparison is 24 benchmark problems selected from collection of cec benchmark problem set the analysis is performed with t test performance index empirical cumulative distribution and time complexity it is concluded that the proposed shpso is a promising new variant of pso which will open new doors of research in this area

a novel differential search algorithm and applications for structure design differential search method is recently proposed to solve box constrained global optimization problems in this paper we will further extend this method to solve generalized constrained optimization problems particularly for structure design optimization problems to handle the constraints we first propose a novel dynamic s type soft threshold penalty method then the original constrained optimization problem is transformed into a sequence of unconstrained optimization problems the proposed method is mainly comprised of two steps parameter iteration and solution iteration the parameter iteration is to update the dynamic penalty parameter through a soft threshold scheme and the solution iteration is to implement differential search algorithm to solve an unconstrained optimization problem two benchmark sets cec2006 and cec2010 and four engineering structure design optimization problems are solved by our proposed algorithm as well as many other swarm based algorithms proposed in recent literatures numerical results show that our method can achieve better performance but with fewer function evaluations comparing with the existing algorithms

stability result with application to adaptive control we prove a relatively simple stability result for a particular system of time varying difference equations we then display its usefulness in analysing the stability and convergence of discrete time adaptive control systems in particular we apply our result to demonstrate the stability and convergence of two disparate adaptive control schemes

a hybrid genetic pattern search augmented lagrangian method for constrained global optimization abstract hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization genetic algorithms as most population based algorithms require a considerable number of function evaluations this may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems thus in order to reduce the total number of function evaluations local and global techniques may be combined moreover the hybridization may provide a more effective trade off between exploitation and exploration of the search space in this study we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented lagrangian function for constraint handling the local search strategy is used to improve the best approximation found by the genetic algorithm convergence to an e global minimizer is proved numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided

improved versions of the lede algorithm for constrained global optimization two modified versions of the authors recent differential evolution algorithm for constrained global optimization are proposed they incorporate a filter set which results in more efficient implementations of the original algorithm numerical results are presented which suggest that the new algorithms are competitive

localised sequential state estimation for advection dominated flows with non gaussian uncertainty description abstract this paper presents a new iterative state estimation algorithm for advection dominated flows with non gaussian uncertainty description of l type uncertain initial condition and model error are assumed to be pointwise bounded in space and time and the observation noise has uncertain but bounded second moments the algorithm approximates this l type bounding set by a union of possibly overlapping ellipsoids which are localised in space on a number of sub domains on each sub domain the state of the original system is estimated by the standard l 2 type filter e g kalman minimax filter which uses gaussian ellipsoidal uncertainty description and observations if any which correspond to this sub domain the resulting local state estimates are stitched together by the iterative d adn schwarz method to reconstruct the state of the original system the efficacy of the proposed method is demonstrated with a set of numerical examples

a novel modified differential evolution algorithm for constrained optimization problems a novel modified differential evolution algorithm nmde is proposed to solve constrained optimization problems in this paper the nmde algorithm modifies scale factor and crossover rate using an adaptive strategy for any solution if it is at a standstill its own scale factor and crossover rate will be adjusted in terms of the information of all successful solutions we can obtain satisfactory feasible solutions for constrained optimization problems by combining the nmde algorithm and a common penalty function method experimental results show that the proposed algorithm can yield better solutions than those reported in the literature for most problems and it can be an efficient alternative to solving constrained optimization problems

a note on teaching learning based optimization algorithm teaching learning based optimization tlbo seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances it is reported that it outperforms some of the well known metaheuristics regarding constrained benchmark functions constrained mechanical design and continuous non linear numerical optimization problems such a breakthrough has steered us towards investigating the secrets of tlbo s dominance this paper reports our findings on tlbo qualitatively and quantitatively through code reviews and experiments respectively our findings have revealed three important mistakes regarding tlbo 1 at least one unreported but important step 2 incorrect formulae on a number of fitness function evaluations and 3 misconceptions about parameter less control additionally unfair experimental settings conditions were used to conduct experimental comparisons e g different stopping criteria the experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy the ultimate goal of this paper is to provide reminders for metaheuristics researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects and to allow fair comparisons of the tlbo algorithm to be made with other metaheuristic algorithms

solving constrained optimization problems using a novel genetic algorithm a novel genetic algorithm is described in this paper for the problem of constrained optimization the algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in the chromosomes the stochastic application of a local search procedure and a stopping rule which is based on asymptotic considerations the algorithm is tested on a series of well known test problems and a comparison is made against the algorithms c somga and donlp2

differential evolution in constrained numerical optimization an empirical study motivated by the recent success of diverse approaches based on differential evolution de to solve constrained numerical optimization problems in this paper the performance of this novel evolutionary algorithm is evaluated three experiments are designed to study the behavior of different de variants on a set of benchmark problems by using different performance measures proposed in the specialized literature the first experiment analyzes the behavior of four de variants in 24 test functions considering dimensionality and the type of constraints of the problem the second experiment presents a more in depth analysis on two de variants by varying two parameters the scale factor f and the population size np which control the convergence of the algorithm from the results obtained a simple but competitive combination of two de variants is proposed and compared against state of the art de based algorithms for constrained optimization in the third experiment the study in this paper shows 1 important information about the behavior of de in constrained search spaces and 2 the role of this knowledge in the correct combination of variants based on their capabilities to generate simple but competitive approaches

an evidential classifier based on feature selection and two step classification strategy in this paper we investigate ways to learn efficiently from uncertain data using belief functions in order to extract more knowledge from imperfect and insufficient information and to improve classification accuracy we propose a supervised learning method composed of a feature selection procedure and a two step classification strategy using training information the proposed feature selection procedure automatically determines the most informative feature subset by minimizing an objective function the proposed two step classification strategy further improves the decision making accuracy by using complementary information obtained during the classification process the performance of the proposed method was evaluated on various synthetic and real datasets a comparison with other classification methods is also presented highlightsa classifier is based on belief functions to tackle uncertain data the classifier composed by feature selection and a two step classification a new combination rule to better represent data uncertainty a new feature selection is based on minimizing uncertainty with sparse constraint two step classification improving accuracy of decision making

structural topology design optimization using genetic algorithms with a bit array representation abstract in this paper a bit array representation method for structural topology optimization using the genetic algorithm ga is implemented the importance of structural connectivity in a design is further emphasized by considering the total number of connected objects of each individual explicitly in an equality constraint function to evaluate the constrained objective function deb s constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the ga a violation penalty method is proposed to drive the ga search towards the topologies with higher structural performance less unusable material and fewer separate objects in the design domain an identical initialization method is also proposed to improve the ga performance in dealing with problems with long narrow design domains numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit array representation method and suggest that the ga performance can be significantly improved by handling the design connectivity properly

closed loop identification for the best asymptotic performance of adaptive robust control discrete linear systems subjected to both additive and parametric deterministic disturbances are considered the certainty equivalence principle is used for the design of adaptive control two different estimation algorithms are proposed to meet different control objectives in the regulation problem with the worst case limsup performance index

a combined nlp differential evolution algorithm approach for the optimization of looped water distribution systems 1 this paper proposes a novel optimization approach for the least cost design of looped water distribution systems wdss three distinct steps are involved in the proposed optimization approach in the first step the shortest distance tree within the looped network is identified using the dijkstra graph theory algorithm for which an extension is proposed to find the shortest distance tree for multisource wdss in the second step a nonlinear programming nlp solver is employed to optimize the pipe diameters for the shortest distance tree chords of the shortest distance tree are allocated the minimum allowable pipe sizes finally in the third step the original looped water network is optimized using a differential evolution de algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two as such the proposed optimization approach combines the traditional deterministic optimization technique of nlp with the emerging evolutionary algorithm de via the proposed network decomposition the proposed methodology has been tested on four looped wdss with the number of decision variables ranging from 21 to 454 results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques

data assimilation for linear parabolic equations minimax projection method in this paper we propose a state estimation method for linear parabolic partial differential equations pde that accounts for errors in the model truncation and observations it is based on an extension of the galerkin projection method the extended method models projection coefficients representing the state of the pde in some basis by means of a differential algebraic equation dae the original estimation problem for the pde is then recast as a state estimation problem for the constructed dae using a linear continuous minimax filter we construct a numerical time integrator that preserves the monotonic decay of a nonstationary lyapunov function along the solution to conclude we demonstrate the efficacy of the proposed method by applying it to the tracking of a discharged pollutant slick in a two dimensional fluid

control of the optimum synthesis process of a four bar linkage whose point on the working member generates the given path this paper presents the optimum synthesis of a four bar linkage in which the coupler point performs a path composed of rectilinear segments and a circular arc the grashof four bar linkage whose geometry provides minimum deviations from the given problem for certain parts of the crank cycle is chosen the motion of the coupler point of the four bar linkage is controlled within the given values of allowed deviations so that it is always in the prescribed environment of the given point on the observed segment the synthesis process tends to bring only those path segments that are beyond the boundaries within the prescribed boundary deviations during the synthesis allowed deviations change from the initial maximum values to the given minimum ones groups of mechanisms realising satisfactory approximation to the desired motion can be obtained by the method of controlled decrease of allowed deviations with the application of the differential evolution de algorithm

a new fuzzy programming approach for multi period portfolio optimization with return demand and risk control abstract this paper considers a multi period portfolio selection problem imposed by return demand and risk control in a fuzzy investment environment in which the returns of assets are characterized by fuzzy numbers a fuzzy lower semi deviation is originally defined as the risk control of portfolio a proportion entropy constraint is added as the divergence measure of portfolio based on the theories of possibility and necessity measures a new multi period portfolio optimization model with return demand and risk control is proposed and then the proposed model is transformed into a crisp nonlinear programming problem by using fuzzy programming approach furthermore an improved differential evolution algorithm is designed for obtaining the optimal strategy finally a numerical example is given to illustrate the practicality and efficiency of the proposed model and the corresponding algorithm

a generalized lqg approach to self tuning control part ii implementation and simulation implementation aspects of the lq self tuner proposed in part i are discussed the method involves solution of a riccati equation through u d factorization and state estimation through transmittance matrices simplified vector formulations of the u d time update and the transmittance matrix are presented simulation results demonstrate the superiority of this algorithm over pole placement and the generalized minimum variance controllers incorporation of the auxiliary variable t instead of y t and the incremental control law u t in the cost function ascribes the right servo properties to this algorithm with offset free control performance the algorithmic stability and numerical robustness without sacrifice of implement ational simplicity make the proposed lq self tuner attractive

evolutionary algorithm for bilevel optimization using approximations of the lower level optimal solution mapping abstract bilevel optimization problems are a class of challenging optimization problems which contain two levels of optimization tasks in these problems the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem such a requirement makes the optimization problem difficult to solve and has kept the researchers busy towards devising methodologies which can efficiently handle the problem despite the efforts there hardly exists any effective methodology which is capable of handling a complex bilevel problem in this paper we introduce bilevel evolutionary algorithm based on quadratic approximations bleaq of optimal lower level variables with respect to the upper level variables the approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for a wide class of bilevel problems the performance of the algorithm has been evaluated on two sets of test problems the first set is a recently proposed smd test set which contains problems with controllable complexities and the second set contains standard test problems collected from the literature the proposed method has been compared against three benchmarks and the performance gain is observed to be significant the codes related to the paper may be accessed from the website http bilevel org

co evolutionary particle swarm optimization to solve constrained optimization problems this paper presents a co evolutionary particle swarm optimization cpso algorithm to solve global nonlinear optimization problems a new co evolutionary pso cpso is constructed in the algorithm a deterministic selection strategy is proposed to ensure the diversity of population meanwhile based on the theory of extrapolation the induction of evolving direction is enhanced by adding a co evolutionary strategy in which the particles make full use of the information each other by using gene adjusting and adaptive focus varied tuning operator infeasible degree selection mechanism is used to handle the constraints a new selection criterion is adopted as tournament rules to select individuals also the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree this diversity mechanism is helpful to guide the search direction towards the feasible region our approach was tested on six problems commonly used in the literature the results obtained are repeatedly closer to the true optimum solution than the other techniques

novel meta heuristic algorithms for clustering web documents clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web recently one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches in this paper we propose novel hybrid harmony search hs based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters by modeling clustering as an optimization problem first we propose a pure harmony search based clustering algorithm that finds near global optimal clusters within a reasonable time then we hybridize k means and harmony clustering in two ways to achieve better clustering experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms

a variable iterated greedy algorithm for the traveling salesman problem with time windows this paper presents a variable iterated greedy algorithm for solving the traveling salesman problem with time windows tsptw to identify a tour minimizing the total travel cost or the makespan separately the tsptw has several practical applications in both production scheduling and logistic operations the proposed algorithm basically relies on a greedy algorithm generating an increasing number of neighboring solutions through the use of the idea of neighborhood change in variable neighborhood search vns algorithms in other words neighboring solutions are generated by destructing a solution component and re constructing the solution again with variable destruction sizes in addition the proposed algorithm is hybridized with a vns algorithm employing backward and forward 1 opt local searches to further enhance the solution quality the performance of the proposed algorithm was tested on several benchmark suites from the literature experimental results confirm that the proposed algorithm is either competitive to or even better than the best performing algorithms from the literature ultimately new best known solutions are obtained for 38 out of 125 problem instances of the recently proposed benchmark suite whereas 15 out of 31 problem instances are also further improved for the makespan criterion

the convergent results about approximating fuzzy random minimum risk problems abstract based on mean chance theory this paper presents a new class of two stage fuzzy random minimum risk problem frmrp in order to solve the two stage frmrp we use a sequence of finitely supported primitive fuzzy random variables to approximate a continuous fuzzy random variable which result in a finite dimensional two stage frmrp we also discuss the convergence of the approximation method including the convergence of the objective value optimal value and optimal solutions to illustrate the modeling idea of this paper we apply the proposed two stage frmrp to the capacitated location allocation problem with fuzzy random demands finally we integrate the approximation method neural network nn and particle swarm optimization pso to design a hybrid pso algorithm to solve the two stage capacitated location allocation problem and provide a numerical example with five facilities and ten customers to demonstrate the effectiveness of the algorithm

improving benders decomposition using a genetic algorithm we develop and investigate the performance of a hybrid solution framework for solving mixed integer linear programming problems benders decomposition and a genetic algorithm are combined to develop a framework to compute feasible solutions we decompose the problem into a master problem and a subproblem a genetic algorithm along with a heuristic are used to obtain feasible solutions to the master problem whereas the subproblem is solved to optimality using a linear programming solver over successive iterations the master problem is refined by adding cutting planes that are implied by the subproblem we compare the performance of the approach against a standard benders decomposition approach as well as against a stand alone solver cplex on miplib test problems

constrained optimization based on modified differential evolution algorithm this paper presents a novel constrained optimization based on modified differential evolution algorithm comde in the new algorithm a new directed mutation rule based on the weighted difference vector between the best and the worst individuals at a particular generation is introduced the new directed mutation rule is combined with the modified basic mutation strategy de rand 1 bin where only one of the two mutation rules is applied with the probability of 0 5 the proposed mutation rule is shown to enhance the local search ability of the basic differential evolution de and to get a better trade off between convergence rate and robustness two new scaling factors are introduced as uniform random variables to improve the diversity of the population and to bias the search direction additionally a dynamic non linear increased crossover probability is utilized to balance the global exploration and local exploitation comde also includes a modified constraint handling technique based on feasibility and the sum of constraints violations a new dynamic tolerance technique to handle equality constraints is also adopted the effectiveness and benefits of the new directed mutation strategy and modified basic strategy used in comde has been experimentally investigated the effect of the parameters of the crossover probability function and the parameters of the dynamic tolerance equation on the performance of comde have been analyzed and evaluated by different experiments numerical experiments on 13 well known benchmark test functions and five engineering design problems have shown that the new approach is efficient effective and robust the comparison results between the comde and the other 28 state of the art evolutionary algorithms indicate that the proposed comde algorithm is competitive with and in some cases superior to other existing algorithms in terms of the quality efficiency convergence rate and robustness of the final solution

a new adaptive law for robust adaptation without persistent excitation a new adaptive law motivated by the work of loannou and kokotovic 1983 is proposed for the robust adaptive control of plants with unknown parameters in this adaptive law the output error plays a dual role in the adjustment of the control parameter vector the advantages of using the adaptive law over others proposed in the literature are discussed in the ideal case the adaptive system has bounded solutions in addition the origin of the error equations is exponentially stable when the reference input is persistently exciting and has a sufficiently large amplitude the adaptive system is also shown to be robust under bounded external disturbances finally it is shown that by suitably modifying the adaptive law the overall system can be made robust in the presence of a class of unmodeled dynamics of the plant simulation results are presented throughout the paper to complement the theoretical developments

solving nonlinearly constrained global optimization problem via an auxiliary function method this paper considers the nonlinearly constrained continuous global minimization problem based on the idea of the penalty function method an auxiliary function which has approximately the same global minimizers as the original problem is constructed an algorithm is developed to minimize the auxiliary function to find an approximate constrained global minimizer of the constrained global minimization problem the algorithm can escape from the previously converged local minimizers and can converge to an approximate global minimizer of the problem asymptotically with probability one numerical experiments show that it is better than some other well known recent methods for constrained global minimization problems

two dimensional optimization of material composition of functionally graded materials using meshless analyses and a genetic algorithm abstract we propose a methodology for the two dimensional simulation and optimization of material distribution of functionally graded materials for thermomechanical processes the proposed approach focuses on metal ceramic functionally graded materials which offer great promise in applications where the operating conditions are severe the two dimensional quasi static heat conduction and thermoelasticity problems are analyzed using the element free galerkin method the spatial distribution of ceramic volume fraction is obtained by piecewise bicubic interpolation of volume fractions defined at a finite number of grid points the effective material properties at a point in the domain are estimated from the local volume fractions of the material constituents using the mori tanaka and self consistent homogenization schemes the volume fraction distribution is optimized using a real coded genetic algorithm the element free galerkin code is validated by comparing the results with an exact thermoelasticity solution for a simply supported plate subsequently we use the genetic algorithm and element free galerkin analyses to optimize the material composition for two model problems in the first problem we minimize the peak residual stress when the functionally graded component is cooled from a high fabrication temperature the goal of the second model problem is to minimize the mass of a graded component with constraints on the peak effective stress and maximum temperature experienced by the metal

on globally stable adaptive control providing l 1 tracking performance for linear discrete time systems abstractexisting adaptive control algorithms at best guarantee that the tracking error is a l2 sequence this paper presents globally stable adaptive control algorithms for linear discrete time systems providing l1 tracking performance two algorithms with different degree of complexity are proposed one for the case of known control directions and a separate algorithm for the case of unknown control directions it is demonstrated that in both cases the tracking error is l1 sequence while the input and output signals are uniformly bounded

theory and design of robust direct and indirect adaptive control schemes a general approach for designing and the theory for analysing robust direct and indirect adaptive control schemes for continuous time plants is presented the design approach involves the development of a general robust adaptive law and the use of the certainty equivalence principle to combine it with robust model reference and pole placement control structures the global stability properties and robustness of the developed adaptive control schemes are established by using a general theory which relates the properties of signals in the mean sense over intervals of time the developed theory and design approach are used to analyse and compare the robustness properties and performance of a wide class of robust adaptive laws which employ a dead zone fixed e1 and a switching modification as well as their variations

a hybrid pso ga algorithm for constrained optimization problems the main objective of this paper is to present a hybrid technique named as a pso ga for solving the constrained optimization problems in this algorithm particle swarm optimization pso operates in the direction of improving the vector while the genetic algorithm ga has been used for modifying the decision vectors using genetic operators the balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators namely crossover and mutation in pso algorithm the constraints defined in the problem are handled with the help of the parameter free penalty function the experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature as shown the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature furthermore experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms

accelerating adaptive trade off model using shrinking space technique for constrained evolutionary optimization adaptive trade off model atm is a constraint handling mechanism proposed recently the main advantages of this model are its simplicity and adaptation moreover it can be easily embedded into evolutionary algorithms for solving constrained optimization problems this paper proposes a novel method for constrained optimization which aims at accelerating the atm using shrinking space technique eighteen benchmark test functions and five engineering design problems are used to test the performance of the method proposed experimental results suggest that combining the atm with the shrinking space technique is very beneficial the method proposed can promptly converge to competitive results without loss of the quality and the precision of the final results performance comparisons with some other state of the art approaches from the literature are also presented

a combined global and local search method to deal with constrained optimization for continuous tabu search heuristic methods such as tabu search are efficient for global optimizations most studies however have focused on constraint free optimizations penalty functions are commonly used to deal with constraints for global optimization algorithms in dealing with constraints this is sometimes inefficient especially for equality constraints as it is difficult to keep the global search within the feasible region by purely adding a penalty to the objective function a combined global and local search method is proposed in this paper to deal with constrained optimizations it is demonstrated by combining continuous tabu search cts and sequential quadratic programming sqp methods first a nested inner and outer loop method is presented to lead the search within the feasible region sqp a typical local search method is used to quickly solve a non linear programming purely for constraints in the inner loop and provides feasible neighbors for the outer loop cts in the outer loop is used to seek for the global optimal finally another local search using sqp is conducted with the results of cts as initials to refine the global search results efficiency is demonstrated by a number of benchmark problems

an efficient constraint handling method for genetic algorithms many real world search and optimization problems involve inequality and or equality constraints and are thus posed as constrained optimization problems in trying to solve constrained optimization problems using genetic algorithms gas or classical optimization methods penalty function methods have been the most popular approach because of their simplicity and ease of implementation however since the penalty function approach is generic and applicable to any type of constraint linear or nonlinear their performance is not always satisfactory thus researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization however the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum in this paper ga s population based approach and ability to make pair wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter careful comparisons among feasible and infeasible solutions are made so as to provide a search direction towards the feasible region once sufficient feasible solutions are found a niching method along with a controlled mutation operator is used to maintain diversity among feasible solutions this allows a real parameter ga s crossover operator to continuously find better feasible solutions gradually leading the search near the true optimum solution gas with this constraint handling approach have been tested on nine problems commonly used in the literature including an engineering design problem in all cases the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier

on the necessity of identifying the true parameter in adaptive lq control in adaptive control problems one may drop the requirement of identifying the true system in order to simplify the problem of control it will be shown that in the adaptive lq control problem this does not at all lead to an easier problem

a self adaptive combined strategies algorithm for constrained optimization using differential evolution there are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems however none of them was considered as being a well accepted approach for solving a broad range of problems with different mathematical properties therefore in this paper for a better coverage of the problem characteristics a self adaptive differential evolution algorithm is introduced to do that it uses multiple search operators in conjunction with multiple constraint handling techniques the need for such an approach is justified by experimental analysis on a well known set of problems the results show that the proposed algorithm is superior to other state of the art algorithms

self adaptive mix of particle swarm methodologies for constrained optimization abstract in recent years many different variants of the particle swarm optimizer pso for solving optimization problems have been proposed however pso has an inherent drawback in handling constrained problems mainly because of its complexity and dependency on parameters furthermore one pso variant may perform well for some test problems but not obtain good results for others in this paper our purpose is to develop a new pso algorithm that can efficiently solve a variety of constrained optimization problems it considers a mix of different pso variants each of which evolves with a different number of individuals from the current population in each generation the algorithm assigns more individuals to the better performing variants and fewer to the worse performing ones also a new pso variant is developed for use in the proposed algorithm to maintain a better balance between its local and global pso versions a new methodology for adapting pso parameters is presented and the proposed self adaptive pso algorithm tested and analyzed on two sets of test problems namely the cec2006 and cec2010 constrained optimization problems based on the results the proposed algorithm shows significantly better performance than the same global and local pso variants as well as other state of the art algorithms although based on our analysis it cannot guarantee an optimal solution for any unknown problem it is expected to be able to solve a wide variety of practical problems

robust adaptive lq control schemes the certainty equivalence principle is used to combine a robust adaptive law with a control structure derived from the linear quadratic lq control problem the resulting adaptive control scheme is applicable to minimum and nonminimum phase continuous time plants and is robust with respect to unmodeled dynamics and bounded disturbances the computational complexity of the continuous time adaptive lq control scheme is improved by using a hybrid adaptive law which requires the solution of an algebraic riccati equation at each interval of time rather than at each time t

stability and convergence analyses of an adaptive gpc based on state space modeling a generalized predictive controller gpc is derived based on a general state space model the equivalence of the predictive control problem to a perturbation problem is revealed in the case of a small perturbation the closed loop poles are calculated with high accuracy for the case of a general perturbation an upper bound on the permissible perturbation norm is derived assuming an open loop stable system both the plant model match and plant model mismatch cases are analysed the controller is proven to be robust and an adaptive implementation is motivated for open loop stable systems the convergence and stability of the control scheme are ensured by proper tuning of the control weight and prediction horizon the results are applicable to a wide range of predictive controllers the main contribution of this paper is not a new control algorithm but new techniques to analyse the gpc as well as new stability and convergence results

decomposition based evolutionary algorithm for large scale constrained problems cooperative coevolutionary algorithms cc have been successful in solving large scale optimization problems the performance of cc can be improved by decreasing the number of interdependent variables among decomposed subproblems this is achieved by first identifying dependent variables and by then grouping them in common subproblems this approach has potential because so far no grouping technique has been mainly developed for constrained problems in this paper a new variable interaction identification technique to identify the dependent variables in large scale constrained problems is proposed the proposed technique is tested on both a new test suite of constrained problems with medium and high dimensions which include overlapping subproblems and different levels of complexity and nonseparability and also the established ded problem the experimental results have shown that the proposed technique contributes to the decomposition approach over a range of high dimensions in comparison with other state of the art grouping techniques it achieves better performance with higher feasibility ratios and less computational time

consolidated optimization algorithm for resource constrained project scheduling problems abstract resource constrained project scheduling problems rcpsps represent an important class of practical problems over the years many optimization algorithms for solving them have been proposed with their performances evaluated using well established test instances with various levels of complexity while it is desirable to obtain a high quality solution and fast rate of convergence from an optimization algorithm no single one performs well across the entire space of instances furthermore even for a given algorithm the optimal choice of its operators and control parameters may vary from one problem to another to deal with this issue we present a generic framework for solving rcpsps in which various meta heuristics each with multiple search operators are self adaptively used during the search process and more emphasis is placed on the better performing algorithms and their underlying search operators to further improve the rate of convergence and introduce good quality solutions into the population earlier a local search approach is introduced the experimental results clearly indicate the capability of the proposed algorithm to attain high quality results using a small population compared with several state of the art algorithms the proposed one delivers the best solutions for problems with 30 and 60 activities and is very competitive for those involving 120 activities

perturbation analysis of gpc with one step control horizon abstract a generalized predictive controller is derived based on a state space model the special but commonly used case of a one step control horizon is analyzed it is shown that the predictive control problem is actually a perturbation problem where there exist easy relations to calculate the closed loop poles in case of a plant model match the perturbed eigenvalues of a stable system as well as a system having one unstable pole are studied the results do not necessarily require the infinite prediction horizon or the zero control weighting assumptions in case of a plant model mismatch stability is studied for general plant representations using operators as well as for a linear time invariant representation it is shown that the generalized predictive controller still yields a mean level controller under some assumptions

a real coded genetic algorithm for solving integer and mixed integer optimization problems in this paper a real coded genetic algorithm named mi lxpm is proposed for solving integer and mixed integer constrained optimization problems the proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm lxpm of deep and thakur k deep m thakur a new crossover operator for real coded genetic algorithms applied mathematics and computation 188 2007 895 912 k deep m thakur a new mutation operator for real coded genetic algorithms applied mathematics and computation 193 2007 211 230 the algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints performance of the algorithm is tested on a set of twenty test problems selected from different sources in literature and compared with the performance of an earlier application of genetic algorithm and also with random search based algorithm rst2anu incorporating annealing concept the proposed mi lxpm outperforms both the algorithms in most of the cases which are considered

investigating a hybrid simulated annealing and local search algorithm for constrained optimization constrained optimization problems cop often take place in many practical applications such as kinematics chemical process optimization power systems and so on these problems are challenging in terms of identifying feasible solutions when constraints are non linear and non convex therefore finding the location of the global optimum in the non convex cop is more difficult as compared to non convex bound constrained global optimization problems this paper proposes a hybrid simulated annealing method hsa for solving the general cop hsa has features that address both feasibility and optimality issues and here it is supported by a local search procedure feasible sequential quadratic programming fsqp we develop two versions of hsa the first version hsap incorporates penalty methods for constraint handling and the second one hsad eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently numerical experiments show that the second version is more reliable in the worst case performance

balanced ranking method for constrained optimization problems using evolutionary algorithms this work presents a new technique to handle constraints in the solution of optimization problems by evolutionary algorithms the balanced ranking method brm in this method the fitness function is based on two rankings for feasible and infeasible solutions respectively the rankings are merged according to deterministic criteria that consider the status of the search process and specific properties of the population the focus of the brm method is to comprise a constraint handling technique cht that is not coupled to the optimization algorithm and thus can be implemented into different algorithms the method is compared with other well known chts that follow this same uncoupled approach all implemented into a canonical genetic algorithm two well known suites of benchmark functions and five engineering problems are used as case studies the performance of the different chts is assessed by nonparametric statistical tests including the sign test and the wilcoxon signed ranks test the results indicate that the brm presents a good performance being reliable and efficient while maintaining its uncoupled characteristic leading to an easy implementation and hybridization with any search algorithm

adaptive configuration of evolutionary algorithms for constrained optimization in the literature many different evolutionary algorithms eas with different search operators have been reported for solving optimization problems however no single algorithm is consistently able to solve all types of problems to overcome this problem the recent trend is to use a mix of operators within a single algorithm there are also cases where multiple methodologies each with a single search operator have been used under one approach these approaches outperformed the single operator based single algorithm approaches in this paper we propose a new algorithm framework that uses multiple methodologies where each methodology uses multiple search operators we introduce it as the ea with adaptive configuration where the first level is to decide the methodologies and the second level is to decide the search operators in this approach all operators and population sizes are updated adaptively although the framework may sound complex one can gain significant benefits from it in solving optimization problems the proposed framework has been tested by solving two sets of specialized benchmark problems the results showed a competitive if not better performance when it was compared to the state of the art algorithms moreover the proposed algorithm significantly reduces the computational time in comparison to both single and multi operator based algorithms

a new meta heuristic algorithm for continuous engineering optimization harmony search theory and practice most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point these algorithms however reveal a limited approach to complicated real world optimization problems if there is more than one local optimum in the problem the result may depend on the selection of an initial point and the obtained optimal solution may not necessarily be the global optimum this paper describes a new harmony search hs meta heuristic algorithm based approach for engineering optimization problems with continuous design variables this recently developed hs algorithm is conceptualized using the musical process of searching for a perfect state of harmony it uses a stochastic random search instead of a gradient search so that derivative information is unnecessary various engineering optimization problems including mathematical function minimization and structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the hs algorithm the results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms 2004 elsevier b v all rights reserved

a natural approach to the numerical integration of riccati differential equations this paper introduces a new class of methods which we call mobius schemes for the numerical solution of matrix riccati differential equations the approach is based on viewing the riccati equation in its natural geometric setting as a flow on the grassmannian of m dimensional subspaces of an n m dimensional vector space since the grassmannians are compact differentiable manifolds and the coefficients of the equation are assumed continuous there are no singularities or intrinsic instabilities in the associated flow the presence of singularities and numerical instabilities is an artifact of the coordinate system but since mobius schemes are based on the natural geometry they are able to deal with numerical instability and pass accurately through the singularities a number of examples are given to demonstrate these properties

on the equality constraints tolerance of constrained optimization problems the tolerance value plays an important role when converting equality constraints into inequality constraints in solving constrained optimization problems many researchers use a fixed or dynamic setting directly based on trial or experiments without systematic study as a well known constraint handling technique deb s feasibility based rule is widely adopted but it has one drawback as the ranking is not consistent with the actual ranking after introducing the tolerance value after carefully analyzing how the tolerance value influences the ranking difference a novel strategy named ranking adjustment strategy ras is proposed which can be considered as a complement of deb s feasibility based rule the experiment has verified the effectiveness of the proposed strategy this is the first time to analyze the inner mechanism of the tolerance value for equality constraints systematically which can give some guide for future research

cooperative coevolutionary differential evolution with improved augmented lagrangian to solve constrained optimisation problems propose the improved augmented lagrangian method ialf to deal with constraints in a more efficient way propose cooperative coevolutionary method using improved augmented lagrangian ccialf approach for solving constrained optimisation problems solve the cec 2006 constrained optimisation test suite with proposed ccialf algorithm with higher solution quality compared to the state of the art algorithms solve practical engineering problems by proposed ccialf algorithm and improve the best known objective function value that exists in the literature in constrained optimisation the augmented lagrangian method is considered as one of the most effective and efficient methods this paper studies the behaviour of augmented lagrangian function alf in the solution space and then proposes an improved augmented lagrangian method we have shown that our proposed method can overcome some of the drawbacks of the conventional augmented lagrangian method with the improved augmented lagrangian approach this paper then proposes a cooperative coevolutionary differential evolution algorithm for solving constrained optimisation problems the proposed algorithm is evaluated on a set of 24 well known benchmark functions and five practical engineering problems experimental results demonstrate that the proposed algorithm outperforms the state of the art algorithms with respect to solution quality as well as efficiency

an effective co evolutionary differential evolution for constrained optimization many practical problems can be formulated as constrained optimization problems due to the simple concept and easy implementation the penalty function method has been one of the most common techniques to handle constraints however the performance of this technique greatly relies on the setting of penalty factors which are usually determined by manual trial and error and the suitable penalty factors are often problem dependent and difficult to set in this paper a differential evolution approach based on a co evolution mechanism named cde is proposed to solve the constrained problems first a special penalty function is designed to handle the constraints second a co evolution model is presented and differential evolution de is employed to perform evolutionary search in spaces of both solutions and penalty factors thus the solutions and penalty factors evolve interactively and self adaptively and both the satisfactory solutions and suitable penalty factors can be obtained simultaneously simulation results based on several benchmark functions and three well known constrained design problems as well as comparisons with some existed methods demonstrate the effectiveness efficiency and robustness of the proposed method

a robust direct adaptive controller this paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics the algorithm is designed based on the reduced order plant which is assumed to be minimum phase and of known order and relative degree but is analyzed with respect to the overall plant which due to the unmodeled dynamics may be nonminimum phase and of unknown order and relative degree it is shown that if the unmodeled dynamics are sufficiently small in the low frequency range then the algorithm guarantees boundedness of all signals in the adaptive loop and small residual tracking errors for any bounded initial conditions in the absence of unmodeled dynamics the residual tracking error is shown to be zero

properties of generalized predictive control control design using long range prediction based on a dynamic model of the plant has become an important contender for high performance applications the model can be derived analytically but more typically it is provided by simple experiments or in the adaptive context by recursive estimation many methods have been proposed in the literature depending on the assumed model structure and the choice of cost function here the approach called generalized predictive control or gpc is reviewed of special interest is the derivation of prediction equations using an observer polynomial and a demonstration that the selection of particular horizons the costing horizons and the control horizon leads to well understood basic techniques such as dead beat pole placement and lq simulated examples show that gpc is suitable for controlling a complex plant such as unstable inverse unstable systems and how the observer polynomial independently tailors the response to the disturbances preprogrammed set points as with robot trajectory control and actuator nonlinearities can also be catered for a discussion of adaptive applications of gpc to several industrial process concludes that the method is easy to use and effective

a hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization numerous optimization methods have been proposed for the solution of the unconstrained optimization problems such as mathematical programming methods stochastic global optimization approaches and metaheuristics in this paper a metaheuristic algorithm called modified shuffled complex evolution msce is proposed where an adaptation of the downhill simplex search strategy combined with the differential evolution method is proposed the efficiency of the new method is analyzed in terms of the mean performance and computational time in comparison with the genetic algorithm using floating point representation gaf and the classical shuffled complex evolution sce ua algorithm using six benchmark optimization functions simulation results and the comparisons with sce ua and gaf indicate that the msce improves the search performance on the five benchmark functions of six tested functions

improved novel global harmony search with a new relaxation method for reliability optimization problems reliability engineers often try to achieve a high reliability level of electronic systems reliability optimization problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint e g the total cost weight the reliability optimization has been an active area of research for the past decades in this paper an improved novel global harmony search inghs approach is proposed to solve reliability optimization problem the inghs employs a stochastic position updating strategy to replace the position updating of nghs and thus the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms performance moreover a new constraint relaxation method also is integrated into the proposed approach applying the proposed approach to several well known benchmark problems results indicate that the inghs algorithm can find better solution effectively and efficiently furthermore the inghs algorithm is used to solve reliability optimization problems numerical results demonstrated that the inghs algorithm performs better than several state of the art algorithms in most cases

a robust indirect adaptive control approach this paper considers the robust design of an indirect adaptive control approach which is applicable when the unknown parameters of a linear time invariant plant lie in a known convex set throughout which no unstable pole zero cancellation occurs in order to achieve the robustness the use of a relative dead zone in the adaptive law is proposed it is shown that with a suitably designed relative dead zone the adaptive control system is globally stable even in the presence of small unmodeled plant uncertainties

truss optimization on shape and sizing with frequency constraints based on orthogonal multi gravitational search algorithm abstract structural optimization on shape and sizing with frequency constraints is well known as a highly nonlinear dynamic optimization problem with several local optimum solutions hence efficient optimization algorithms should be utilized to solve this problem in this study orthogonal multi gravitational search algorithm omgsa as a meta heuristic algorithm is introduced to solve truss optimization on shape and sizing with frequency constraints the omgsa is a hybrid approach based on a combination of multi gravitational search algorithm multi gsa and an orthogonal crossover oc in multi gsa the population is split into several sub populations then each sub population is independently evaluated by an improved gravitational search algorithm igsa furthermore the oc is used in the proposed omgsa in order to find and exploit the global solution in the search space the capability of omgsa is demonstrated through six benchmark examples numerical results show that the proposed omgsa outperform the other optimization techniques

accelerated random search for constrained global optimization assisted by radial basis function surrogates abstract the accelerated random search ars algorithm appel et al 2004 is a stochastic algorithm for the global optimization of black box functions subject only to bound constraints ars iteratively samples uniformly within a box centered at the current best point if the sample point is worse than the current best point then the size of the box is reduced thereby making it more likely to generate an improving solution if the current best point is not yet a local minimum otherwise if the sample point is an improvement over the current best point then the size of the box is reset to the initial value so that the box covers the entire search space ars has been shown theoretically and numerically to converge to the global minimum faster than the pure random search prs algorithm on bound constrained problems this article develops the constrained ars cars algorithm which is an extension of ars for optimization problems with black box inequality constraints under some mild conditions on the constrained optimization problem we prove the convergence to the global minimum in a probabilistic sense of a class of stochastic search algorithms called scattered uniform random search scat urs algorithms which includes cars as a special case to improve performance of cars on computationally expensive simulation based problems we incorporate radial basis function rbf surrogate models to approximate the objective and constrained functions and refer to the resulting algorithm as cars rbf numerical experiments show that cars consistently and substantially outperforms both the pure random search prs and accelerated particle swarm optimization apso algorithms yang 2010 on 31 test problems with dimensions ranging from 2 to 20 moreover cars rbf is an improvement over cars outperforms a sequential penalty derivative algorithm and competes with constrlmsrbf regis 2011 on the same test problems finally sensitivity analysis of cars rbf to the type of rbf model used shows that the cubic rbf model generally yields the best results on the test problems among six types of rbf models considered including the thin plate spline gaussian and multiquadric models

an improved harmony search algorithm for solving optimization problems this paper develops an improved harmony search ihs algorithm for solving optimization problems ihs employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search hs algorithm in this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented the ihs algorithm has been successfully applied to various benchmarking and standard engineering optimization problems numerical results reveal that the proposed algorithm can find better solutions when compared to hs and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems

constraints handling in nash adjoint optimization methods for multi objective aerodynamic design abstract game theory and its particular nash games are gaining importance in multi objective optimization in engineering problems over the past decade among ongoing advances in optimization methods and tools many applications including mathematical modeling with constraints in different areas remain challenges in industrial design environments this paper describes a constraint handling algorithm to extend the use of nash games to a more realistic multi objective aerodynamic optimization when taking into account constraints a competitive nash game with constraints is adapted to a cooperative game in which nash procedure acts as a sub game to calculate the equilibrium point and collaborates with a player in charge of constraints the partial gradient of each objective instead of design variables is used as elitist information exchanged symmetrically in sub game existence and equivalence of the solution are analyzed and proved based on brouwer fixed point theorem numerical experiments on 2d multi objective aerodynamic optimization with lift and or geometry constraints are implemented and results show that constraints are satisfied

single row facility layout problem using a permutation based genetic algorithm in this paper a permutation based genetic algorithm ga is applied to the np hard problem of arranging a number of facilities on a line with minimum cost known as the single row facility layout problem srflp the ga individuals are obtained by using some rule based as well as random permutations of the facilities which are then improved towards the optimum by means of specially designed crossover and mutation operators such schemes led the ga to handle the srflp as an unconstrained optimization problem in the computational experiments carried out with large size instances of sizes from 60 to 80 available in the literature the proposed ga improved several previously known best solutions

adaptive robust control for siso systems abstract this paper addresses the problem of nonconservative adaptive robust regulating a siso system allowing for model perturbations in the coprime factor representation of the system as well as external additive bounded disturbance signals the perturbations and disturbances are assumed to belong to a particular parameterized class and are norm bounded the norm bounds are not available for control design but the class knowledge is available the performance measure is the worst possible steady state absolute value of the controlled output over the class of allowable disturbances and model perturbations an identification based adaptive control method is proposed where the identification criterion coincides with the control objective the proposed adaptive scheme achieves near optimal performance simulations comparing the new scheme with an adaptive controller based on classical recursive least squares identification are presented

an improved vector particle swarm optimization for constrained optimization problems increasing attention is being paid to solve constrained optimization problems cop frequently encountered in real world applications in this paper an improved vector particle swarm optimization ivpso algorithm is proposed to solve cops the constraint handling technique is based on the simple constraint preserving method velocity and position of each particle as well as the corresponding changes are all expressed as vectors in order to present the optimization procedure in a more intuitively comprehensible manner the nvpso algorithm 30 which uses one dimensional search approaches to find a new feasible position on the flying trajectory of the particle when it escapes from the feasible region has been proposed to solve cop experimental results showed that searching only on the flying trajectory for a feasible position influenced the diversity of the swarm and thus reduced the global search capability of the nvpso algorithm in order to avoid neglecting any worthy position in the feasible region and improve the optimization efficiency a multi dimensional search algorithm is proposed to search within a local region for a new feasible position the local region is composed of all dimensions of the escaped particle s parent and the current positions obviously the flying trajectory of the particle is also included in this local region the new position is not only present in the feasible region but also has a better fitness value in this local region the performance of ivpso is tested on 13 well known benchmark functions experimental results prove that the proposed ivpso algorithm is simple competitive and stable

hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems this study presents a hybrid harmony search algorithm hhsa to solve engineering optimization problems with continuous design variables although the harmony search algorithm hsa has proven its ability of finding near global regions within a reasonable time it is comparatively inefficient in performing local search in this study sequential quadratic programming sqp is employed to speed up local search and improve precision of the hsa solutions moreover an empirical study is performed in order to determine the impact of various parameters of the hsa on convergence behavior various benchmark engineering optimization problems are used to illustrate the effectiveness and robustness of the proposed algorithm numerical results reveal that the proposed hybrid algorithm in most cases is more effective than the hsa and other meta heuristic or deterministic methods

design issues in adaptive control an integrated approach to the design of practical adaptive control algorithms is presented many existing ideas are brought together and the effect of various design parameters available to a user is explored the theory is extended by showing how the problem of stabilizability of the estimated model can be overcome by running parallel estimators it is shown how asymptotic tracking of deterministic set points can be achieved in the presence of unmodeled dynamics

an adaptive lq controller for non minimum phase systems abstract the paper studied the problem of the adaptive tracking of a reference signal by the output of any deterministic discrete siso linear system with unknown parameters an adaptive control scheme based on the lq control solution is proposed the stability of the resulting adaptively controlled system and conditions for a good tracking are studied several simulations illustrate the proposal

a generalized lqg approach to self tuning control part i aspects of design implicit self tuners based on single stage cost function minimization schemes require exact knowledge of the dead time of the controlled plant explicit pole placement self tuners on the other hand though robust against variations of the time delay are very sensitive to the assumptions made about the model order of the plant wrong assumptions may result in very poor performance an explicit method based on minimization of a multistage cost function and a carima plant model formulated in state space is presented the method extends the design features of the generalized minimum variance scheme to the multistage case these can be used to tailor the closed loop response of the system the approach is shown to be robust against wrong a priori assumptions made about the plant dead time or order thus maintaining good servo and disturbance rejection properties see part ii

particle swarm optimization hybridization perspectives and experimental illustrations metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods in the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic particle swarm optimization or pso hybridization is a method of combining two or more techniques in a judicious manner such that the resulting algorithm contains the positive features of both or all the algorithms depending on the algorithm s used we made three classifications as i hybridization of pso and genetic algorithms ii hybridization of pso with differential evolution and iii hybridization of pso with other techniques where other techniques include various local and global search methods besides giving the review we also show a comparison of three hybrid pso algorithms hybrid differential evolution particle swarm optimization de pso adaptive mutation particle swarm optimization ampso and hybrid genetic algorithm particle swarm optimization ga pso on a test suite of nine conventional benchmark problems

cultured differential evolution for constrained optimization a cultural algorithm with a differential evolution population is proposed in this paper this cultural algorithm uses different knowledge sources to influence the variation operator of the differential evolution algorithm in order to reduce the number of fitness function evaluations required to obtain competitive results comparisons are provided with respect to three techniques that are representative of the state of the art in the area the results obtained by our algorithm are similar in quality to those obtained by the other approaches with respect to which it was compared however our approach requires a lower number of fitness function evaluations than the others