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

an exact algorithm for higher dimensional orthogonal packing higher dimensional orthogonal packing problems have a wide range of practical applications including packing cutting and scheduling combining the use of our data structure for characterizing feasible packings with our new classes of lower bounds and other heuristics we develop a two level tree search algorithm for solving higher dimensional packing problems to optimality computational results are reported including optimal solutions for all two dimensional test problems from recent literature this is the third in a series of articles describing new approaches to higher dimensional packing

new lower bounds for bin packing problems with conflicts abstract the bin packing problem with conflicts bpc consists of minimizing the number of bins used to pack a set of items where some items cannot be packed together in the same bin due to compatibility restrictions the concepts of dual feasible functions dff and data dependent dual feasible functions ddff have been used in the literature to improve the resolution of several cutting and packing problems in this paper we propose a general framework for deriving new ddff as well as a new concept of generalized data dependent dual feasible functions gddff a conflict generalization of ddff the gddff take into account the structure of the conflict graph using the techniques of graph triangulation and tree decomposition then we show how these techniques can be used in order to improve the existing lower bounds

a hybrid simplex search and particle swarm optimization for unconstrained optimization this paper proposes the hybrid nm pso algorithm based on the nelder mead nm simplex search method and particle swarm optimization pso for unconstrained optimization nm pso is very easy to implement in practice since it does not require gradient computation the modification of both the nelder mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence the main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy in a suite of 20 test function problems taken from the literature computational results via a comprehensive experimental study preceded by the investigation of parameter selection show that the hybrid nm pso approach outperforms other three relevant search techniques i e the original nm simplex search method the original pso and the guaranteed convergence particle swarm optimization gcpso in terms of solution quality and convergence rate in a later part of the comparative experiment the nm pso algorithm is compared to various most up to date cooperative pso cpso procedures appearing in the literature the comparison report still largely favors the nm pso algorithm in the performance of accuracy robustness and function evaluation as evidenced by the overall assessment based on two kinds of computational experience the new algorithm has demonstrated to be extremely effective and efficient at locating best practice optimal solutions for unconstrained optimization

moving horizon state estimation for nonlinear systems using neural networks moving horizon mh state estimation is addressed for nonlinear discrete time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding window least squares cost function such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design thanks to the offline optimization of the parameters the resulting mh estimation scheme requires a reduced online computational effort simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques

brief constrained linear state estimation a moving horizon approach this article considers moving horizon strategies for constrained linear state estimation additional information for estimating state variables from output measurements is often available in the form of inequality constraints on states noise and other variables formulating a linear state estimation problem with inequality constraints however prevents recursive solutions such as kalman filtering and consequently the estimation problem grows with time as more measurements become available to bound the problem size we explore moving horizon strategies for constrained linear state estimation in this work we discuss some practical and theoretical properties of moving horizon estimation we derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate we also discuss smoothing strategies for moving horizon estimation our framework is solely deterministic

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

new classes of fast lower bounds for bin packing problems the bin packing problem is one of the classical np hard optimization problems in this paper we present a simple generic approach for obtaining new fast lower bounds based on dual feasible functions worst case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by martello and toth which can be understood as a special case in particular we prove an asymptotic worst case performance of 3 4 for a bound that can be computed in linear time for items sorted by size in addition our approach provides a general framework for establishing new bounds

an exact algorithm for the two dimensional strip packing problem this paper considers the two dimensional strip packing problem 2sp in which a set of rectangular items have to be orthogonally packed without overlapping into a strip of a given width and infinite height by minimizing the overall height of the packing the 2sp is np hard in the strong sense and finds many practical applications we propose reduction procedures lower and upper bounds and an exact algorithm for the 2sp the new lower bounds are both combinatorial bounds and bounds derived from different relaxations of mathematical formulations of the 2sp the new upper bounds are obtained by constructive heuristics based on different strategies to place the items into the strip the new exact method is based on a branch and bound approach computational results on different sets of test problems derived from the literature show the effectiveness of the new lower and upper bounds and of the new exact algorithm

receding horizon estimation for discrete time linear systems the problem of estimating the state of a discrete time linear system can be addressed by minimizing an estimation cost function dependent on a batch of recent measure and input vectors this problem has been solved by introducing a receding horizon objective function that includes also a weighted penalty term related to the prediction of the state for such an estimator convergence results and unbiasedness properties have been proved the issues concerning the design of this filter are discussed in terms of the choice of the free parameters in the cost function the performance of the proposed receding horizon filter is evaluated and compared with other techniques by means of a numerical example

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

teaching learning based optimization with dynamic group strategy for global optimization abstract global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence in recent years there have been some significant developments in these areas regarding the solution of global optimization problems in this paper we propose an improved teaching learning based optimization tlbo algorithm with dynamic group strategy dgs for global optimization problems different to the original tlbo algorithm dgstlbo enables each learner to learn from the mean of his corresponding group rather than the mean of the class in the teacher phase furthermore each learner employs the random learning strategy or the quantum behaved learning strategy in his corresponding group in the learner phase regrouping occurs dynamically after a certain number of generations helping to maintain the diversity of the population and discourage premature convergence to verify the feasibility and effectiveness of the proposed algorithm experiments are conducted on 18 numerical benchmark functions in 10 30 and 50 dimensions the results show that the proposed dgstlbo algorithm is an effective method for global optimization problems

observer design for nonlinear systems with discrete time measurements this paper focuses on the development of asymptotic observers for nonlinear discrete time systems it is argued that instead of trying to imitate the linear observer theory the problem of constructing a nonlinear observer can be more fruitfully studied in the context of solving simultaneous nonlinear equations in particular it is shown that the discrete newton method properly interpreted yields an asymptotic observer for a large class of discrete time systems while the continuous newton method may be employed to obtain a global observer furthermore it is analyzed how the use of broyden s method in the observer structure affects the observer s performance and its computational complexity an example illustrates some aspects of the proposed methods moreover it serves to show that these methods apply equally well to discrete time systems and to continuous time systems with sampled outputs

survey constrained model predictive control stability and optimality model predictive control is a form of control in which the current control action is obtained by solving at each sampling instant a finite horizon open loop optimal control problem using the current state of the plant as the initial state the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant an important advantage of this type of control is its ability to cope with hard constraints on controls and states it has therefore been widely applied in petro chemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls in this review we focus on model predictive control of constrained systems both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and or time varying systems we concentrate our attention on research dealing with stability and optimality in these areas the subject has developed in our opinion to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control we distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature in some cases the finite horizon optimal control problem solved on line is exactly equivalent to the same problem with an infinite horizon in other cases it is equivalent to a modified infinite horizon optimal control problem in both situations known advantages of infinite horizon optimal control accrue

moving horizon estimation for hybrid systems we propose a state smoothing algorithm for hybrid systems based on moving horizon estimation mhe by exploiting the equivalence between hybrid systems modeled in the mixed logic dynamical form and piecewise affine systems we provide sufficient conditions on the time horizon and the penalties on the state at the beginning of the estimation horizon to guarantee asymptotic convergence of the mhe scheme moreover we propose two practical algorithms for the computation of penalties that allow us to implement mhe by solving a mixed integer quadratic program

further results on nonlinear receding horizon observers further results are proposed that concern the design and the convergence of receding horizon nonlinear observers the key feature is the definition of observability radius in relation with a prespecified compact set of initial configurations this enables a semiglobal convergence result to be derived that turns out to be a global convergence result when appropriate regularity assumptions are made a simple example is proposed to illustrate the basic features of the note

lagrangian duality between constrained estimation and control we show that the lagrangian dual of a constrained linear estimation problem is a particular nonlinear optimal control problem the result has an elegant symmetry which is revealed when the constrained estimation problem is expressed as an equivalent nonlinear optimisation problem the results extend and enhance known connections between the linear quadratic regulator and linear quadratic state estimation problems

two dimensional packing problems a survey we consider problems requiring to allocate a set of rectangular items to larger rectangular standardized units by minimizing the waste in two dimensional bin packing problems these units are finite rectangles and the objective is to pack all the items into the minimum number of units while in two dimensional strip packing problems there is a single standardized unit of given width and the objective is to pack all the items within the minimum height we discuss mathematical models and survey lower bounds classical approximation algorithms recent heuristic and metaheuristic methods and exact enumerative approaches the relevant special cases where the items have to be packed into rows forming levels are also discussed in detail

how novel is the novel black hole optimization approach due to abundance of novel optimization algorithms in recent years the problem of large similarities among methods that are named differently is becoming troublesome and general the question arises if the novel source of inspiration is sufficient to breed an optimization algorithm with a novel name even if its search properties are almost the same as or are even a simplified variant of the search properties of an older and well known method in this paper it is rigidly shown that the recently proposed heuristic approach called the black hole optimization is in fact a simplified version of particle swarm optimization with inertia weight additionally because a large number of metaheuristics developed during the last decade is claimed to be nature inspired a short discussion on inspirations of optimization algorithms is presented

a solution for the partitioning problem in partition based moving horizon estimation in addition to being observable systems composed of interconnected subsystems must typically exhibit a particular subsystem structure if their state is to be estimated successfully by a partition based moving horizon estimator pmhe suitable subsystem structures also called system partitionings can be unknown may not be desirable for a particular application or may not exist at all in this contribution we present the first pmhe without any restrictions on the system partitioning we analyze the convergence of its iterations at each time step and the stability of its estimation error dynamics the benefits of our solution are illustrated on a numerical example

on estimation error bounds for receding horizon filters using quadratic boundedness quadratic boundedness is used to deal with stability and design of receding horizon estimators upper bounds on the norm of the estimation error have been found by means of invariant sets that can be constructed by using quadratic boundedness moreover these bounds are expressed in terms of linear matrix inequalities and are well suited to being minimized for the purpose of design

a combined moving horizon and direct virtual sensor approach for constrained nonlinear estimation this paper presents a novel approach to design estimators for nonlinear systems the approach is based on a combination of linear moving horizon estimation mhe and direct virtual sensor dvs techniques and allows the design of estimators with guaranteed stability which can account for convex constraints on the variables to be estimated it is also shown that the designed estimators are optimal in the sense that they give minimal worst case estimation error on the basis of the available finite number of noise corrupted data with respect to an ideal mhe filter obtained by assuming exact knowledge of the system dynamics and of the global solution of the related nonlinear program the approach is tested on a nonlinear mass spring damper system

simultaneous nonlinear model predictive control and state estimation an output feedback approach to model predictive control that combines state estimation and control into a single minmax optimization is introduced for discrete time nonlinear systems specifically a criterion that involves finite forward and backward horizons is minimized with respect to control input variables and is maximized with respect to the unknown initial state as well as disturbance and measurement noise variables under appropriate assumptions that encode controllability and observability we show that the state of the closed loop remains bounded and that a bound on tracking error can be found for trajectory tracking problems we also introduce a primaldual interior point method that can be used to efficiently solve the minmax optimization problem and show in simulation examples that the method succeeds even for severely nonlinear and non convex problems

convergence and stability of a constrained partition based moving horizon estimator a novel type of iterative partition based moving horizon estimators pmhe is proposed the estimates of which approach those of a centralized moving horizon estimator as the number of iterations increases it uses a deterministic setting and can handle known inputs as well as bounds on the estimated state we derive conditions on the system its partitions and the scalar regularization parameter which guarantee convergence towards the optimal centralized state estimate as well as stability of the estimation error dynamics even with a finite number of iterations at each time step finally numerical simulations demonstrate both the features and competitive estimation quality of the proposed method

the three dimensional knapsack problem with balancing constraints abstract in this paper we introduce a new packing problem the three dimensional knapsack problem with balancing constraints 3bkp the extension of the three dimensional knapsack problem 3kp where additional constraints related to the packing center of mass are given the 3bkp consists in orthogonally packing a subset of three dimensional weighted items into a knapsack in order to maximize the total profit of the loaded items the items must not overlap and the packing center of mass must lie into a predefined boxed domain inside the knapsack we assume that items can be rotated we give a mip model for the problem upper bounds and an efficient heuristic to solve large size instances the computational results show that the mip model cannot find optimal solutions except for small size instances but it can be used to calculate upper and lower bounds it is shown that our heuristic outperforms the solution quality both of the mip model and the heuristics available in the literature explicitly designed to solve the 3kp

a new identification framework for off line computation of moving horizon observers in this technical note a new nonlinear identification framework is proposed to address the issue of off line computation of moving horizon observer estimate the proposed structure merges the advantages of nonlinear approximators with the efficient computation of constrained quadratic programming problems a bound on the estimation error is proposed and the efficiency of the resulting scheme is illustrated using two state estimation examples

enhancing the search ability of differential evolution through orthogonal crossover differential evolution de is a class of simple yet powerful evolutionary algorithms for global numerical optimization binomial crossover and exponential crossover are two commonly used crossover operators in current popular de it is noteworthy that these two operators can only generate a vertex of a hyper rectangle defined by the mutant and target vectors therefore the search ability of de may be limited orthogonal crossover ox operators which are based on orthogonal design can make a systematic and rational search in a region defined by the parent solutions in this paper we have suggested a framework for using an ox in de variants and proposed oxde a combination of de rand 1 bin and ox extensive experiments have been carried out to study oxde and to demonstrate that our framework can also be used for improving the performance of other de variants

neural approximators for nonlinear finite memory state estimation no general analytical tools are available to estimate the state of a nonlinear stochastic system observed through a nonlinear noisy channel this problem is addressed in this paper under the assumption that the statistics of the random variables are unknown hence a statistical approach is followed instead of a probabilistic one the following approximations are enforced i the state estimator is a finite memory one ii the estimation functions are given fixed structures in which a certain number of parameters have to be optimized multilayer feedforward neural networks are chosen from among various possible nonlinear approximators iii the algorithms for optimizing the parameters i e the network weights rely on a stochastic approximation simulation results are reported to compare the behaviour of the proposed estimator with the extended kalman filter and the estimators based on the on line minimization of the estimation error

an exact algorithm for the two dimensional orthogonal packing problem with unloading constraints this paper describes an exact algorithm for solving a two dimensional orthogonal packing problem with unloading constraints which occurs as a subproblem of mixed vehicle routing and loading problems the packing considered in this work is basically a feasibility problem involving a single bin the problem is addressed through a decomposition approach wherein a branch and cut algorithm is designed for solving a one dimensional relaxation of the original problem when an integer solution is found in the branching tree a subsidiary problem is solved to identify a two dimensional packing that does not lead to any overlap and satisfies the unloading constraints cuts are added when the subsidiary problem proves to be infeasible several preprocessing techniques aimed at reducing the size of the solution space and uncovering infeasibility are also described a numerical comparison with the best known exact method is reported at the end based on benchmark instances

exact solution of the two dimensional finite bon packing problem given a set of rectangular pieces to be cut from an unlimited number of standardized stock pieces bins the two dimensional finite bin packing problem is to determine the minimum number of stock pieces that provide all the pieces the problem is np hard in the strong sense and finds many practical applications in the cutting and packing area we analyze a well known lower bound and determine its worst case performance we propose not lower bounds which are used within a branch and bound algorithm for the exact solution of the problem extensive computational testing on problem instances from the literature involving up to 120 pieces shows the effectiveness of the proposed approach

the stability of nonlinear least squares problems and the cramer rao bound a number of problems of interest in signal processing can be reduced to nonlinear parameter estimation problems the traditional approach to studying the stability of these estimation problems is to demonstrate finiteness of the cramer rao bound crb for a given noise distribution we review an alternate deterministic notion of stability for the associated nonlinear least squares nls problem from the realm of nonlinear programming i e that the global minimizer of the least squares problem exists and varies smoothly with the noise furthermore we show that under mild conditions identifiability of the parameters along with a finite crb for the case of gaussian noise is equivalent to the deterministic stability of the nls problem finally we demonstrate the application of our result which is general to the problems of multichannel blind deconvolution and sinusoid retrieval to generate new stability results for these problems with little additional effort

a comparison of complete global optimization solvers results are reported of testing a number of existing state of the art solvers for global constrained optimization and constraint satisfaction on a set of over 1000 test problems in up to 1000 variables collected from the literature the test problems are available online in ampl and were translated into the input formats of the various solvers using routines from the coconut environment these translators are available online too

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 note on multi objective improved teaching learning based optimization algorithm mo itlbo recently multi objective improved teaching learning based optimization algorithm mo itlbo has been proposed to solve complex multi objective optimization problems and has been shown to be competitive against various other state of the art algorithms the algorithm was demonstrated on the constrained and unconstrained optimization problems of cec 2009 and was reported to have shown impressive results however some critical steps in the algorithm have not been adequately described and these have become major impediments even for the implementation of mo itlbo in this note we have explained all such issues which need to be convincingly addressed so that independent researchers could evaluate and use mo itlbo for various other applications also two variants of mo itlbo have been suggested whose results enforce that the issues reported in this article are critical to harness the reported benefits of the mo itlbo

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

state observation by on line minimization a new approach is presented to observing the state of a nonlinear control system the class considered consists of nonlinear observable systems described by an ordinary differential equation in combination with a measurement equation the system functions are required to be twice differentiable based on newton s method an iterative process of state approximation is derived the algorithm computes a piecewise continuous function which is an approximation of the true state the convergence of the proposed algorithm is proved finally several examples show the performance characteristics of the method

distributed moving horizon estimation for linear constrained systems this paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation under weak observability conditions we prove convergence of the state estimates computed by any sensors to the correct state even when constraints on noise and state variables are taken into account in the estimation process simulation examples are provided in order to show the main features of the proposed method

teaching learning based optimization with global crossover for global optimization problems teaching learning based optimization tlbo is a newly developed population based meta heuristic algorithm it has better global searching capability but it also easily got stuck on local optima when solving global optimization problems this paper develops a new variant of tlbo called teaching learning based optimization with global crossover tlbo gc for improving the performance of tlbo in teaching phase a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima and a new global crossover strategy is incorporated into the learning phase which aims at balancing local and global searching effectively the performance of tlbo gc is assessed by solving global optimization functions with different characteristics compared to the tlbo several modified tlbos and other promising heuristic methods numerical results reveal that the tlbo gc has better optimization performance

a combinatorial characterization of higher dimensional orthogonal packing higher dimensional orthogonal packing problems have a wide range of practical applications including packing cutting and scheduling previous efforts for exact algorithms have been unable to avoid structural problems that appear for instance in two or higher dimensional space we present a new approach for modeling packings using a graph theoretical characterization of feasible packings our characterization allows it to deal with classes of packings that share a certain combinatorial structure instead of having to consider one packing at a time in addition we can make use of elegant algorithmic properties of certain classes of graphs this allows our characterization to be the basis for a successful branch and bound framework this is the first in a series of papers describing new approaches to higher dimensional packing

moving horizon observers and observer based control in this paper two topics are explored a new approach to the problem of obtaining an estimate of the state of a nonlinear system is proposed the moving horizon observer produces an estimate of the state of the nonlinear system at time t either by minimizing or approximately minimizing a cost function over the preceding interval horizon t t t as t advances so does the horizon convergence of the estimator is established under the assumption that the corresponding global optimization problem can be approximately solved and a uniform reconstructability condition is satisfied the latter condition is automatically satisfied for linear observable systems the utility of the estimator for receding horizon control is explored in particular stability of a composite moving horizon system comprising a moving horizon regulator and a moving horizon observer is established

improving the efficiency of a best first bottom up approach for the constrained 2d cutting problem abstract this work introduces several improvements in the solution of the constrained 2d cutting problem such improvements combine the detection of dominated and duplicated cutting patterns with the implementation of parallel approaches for best first search methods the analysis of symmetries and dominances among the cutting patterns is able to discard some non optimal or redundant builds thus reducing the search space to be explored the experimental evaluation demonstrates that when the domination duplication rules are applied to an efficient parallel approach the obtained reduction in the number of managed nodes involves a noticeable decrease in the computational effort associated with the final search process

optimization based non linear observers revisited optimization based non linear observers are those where the integrated output prediction error is used to define the dynamic of the observer s state their main advantage is genericity and the natural assumptions that are needed for the related convergence results to hold here we present some new theoretical results concerning these observers despite the title we use our observer is not based on the search for a global minimum of a cost function it is based on a descent like approach without the use of the hessian matrix a nice feature is that the resulting observer takes a classical form of in the sense that it is defined by a set of ordinary differential equations besides the theoretical results some implementation issues are discussed in particular we propose to use the post stabilization approach well known when dealing with differential systems with invariant submanifolds this enables to increase the sampling period while keeping very good precision two illustrative examples are presente

robust receding horizon state estimation for uncertain discrete time linear systems an approach to robust receding horizon state estimation for discrete time linear systems is presented estimates of the state variables can be obtained by minimizing a worst case quadratic cost function according to a sliding window strategy this leads to state the estimation problem in the form of a regularized least squares one with uncertain data the optimal solution involving on line scalar minimization together with a suitable closed form approximation are given the stability properties of the estimation error for both the optimal filter and the approximate one have been studied and conditions to select the design parameters are proposed simulation results are reported to show the effectiveness of the proposed approach

stochastic stability of the discrete time extended kalman filter the authors analyze the error behavior for the discrete time extended kalman filter for general nonlinear systems in a stochastic framework in particular it is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough this result is verified by numerical simulations for an example system

a new exact method for the two dimensional orthogonal packing problem abstract the two dimensional orthogonal packing problem 2 opp consists in determining if a set of rectangles items can be packed into one rectangle of fixed size bin in this paper we propose two exact algorithms for solving this problem the first algorithm is an improvement on a classical branch bound method whereas the second algorithm is based on a new relaxation of the problem we also describe reduction procedures and lower bounds which can be used within enumerative methods we report computational experiments for randomly generated benchmarks which demonstrate the efficiency of both methods the second method is competitive compared to the best previous methods it can be seen that our new relaxation allows an efficient detection of non feasible instances

an exact algorithm for general orthogonal two dimensional knapsack problems abstract we present a new exact tree search procedure for solving two dimensional knapsack problems in which a number of small rectangular pieces each of a given size and value are required to be cut from a large rectangular stock plate the objective is to maximise the value of pieces cut or minimise the wastage we consider the case where there is a maximum number of times that a piece may be used in a cutting pattern the algorithm limits the size of the tree search by using a bound derived from a langrangean relaxation of a 0 1 integer programming formulaton of the problem subgradient optimisation is used to optimise this bound reduction tests derived from both the original problem and the lagrangean relaxation produce substantial computational gains the computational performance of the algorithm indicates that it is an effective procedure capable of solving optimally practical two dimensional cutting problems of medium size

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

constrained state estimation for nonlinear discrete time systems stability and moving horizon approximations state estimator design for a nonlinear discrete time system is a challenging problem further complicated when additional physical insight is available in the form of inequality constraints on the state variables and disturbances one strategy for constrained state estimation is to employ online optimization using a moving horizon approximation we propose a general theory for constrained moving horizon estimation sufficient conditions for asymptotic and bounded stability are established we apply these results to develop a practical algorithm for constrained linear and nonlinear state estimation examples are used to illustrate the benefits of constrained state estimation our framework is deterministic

moving horizon observer with regularisation for detectable systems without persistence of excitation a constrained moving horizon observer is developed for nonlinear discrete time systems the algorithm is proved to converge exponentially under a detectability assumption with the data being exciting at all times however in many practical estimation problems such as combined state and parameter estimation the data may not be exciting for every period of time the algorithm therefore has regularisation mechanisms to ensure robustness and graceful degradation of performance in cases when the data are not exciting this includes the use of a priori estimates in the moving horizon cost function and the use of thresholded singular value decomposition to avoid ill conditioned or ill posed inversion of the associated nonlinear algebraic equations that define the moving horizon cost function the latter regularisation relies on monitoring of the rank of an estimate of a hessian like matrix and conditions for uniform exponential convergence are given the method is in particular useful with augmented state sp

moving horizon state estimation for nonlinear discrete time systems new stability results and approximation schemes a moving horizon state estimation problem is addressed for a class of nonlinear discrete time systems with bounded noises acting on the system and measurement equations as the statistics of such disturbances and of the initial state are assumed to be unknown we use a generalized least squares approach that consists in minimizing a quadratic estimation cost function defined on a recent batch of inputs and outputs according to a sliding window strategy for the resulting estimator the existence of bounding sequences on the estimation error is proved in the absence of noises exponential convergence to zero is obtained moreover suboptimal solutions are sought for which a certain error is admitted with respect to the optimal cost value the approximate solution can be determined either on line by directly minimizing the cost function or off line by using a nonlinear parameterized function simulation results are presented to show the effectiveness of the proposed approach in comparison with the extended kalman filter

a neural state estimator with bounded errors for nonlinear systems a neural state estimator is described acting on discrete time nonlinear systems with noisy measurement channels a sliding window quadratic estimation cost function is considered and the measurement noise is assumed to be additive no probabilistic assumptions are made on the measurement noise nor on the initial state novel theoretical convergence results are developed for the error bounds of both the optimal and the neural approximate estimators to ensure the convergence properties of the neural estimator a minimax tuning technique is used the approximate estimator can be designed offline in such a way as to enable it to process on line any possible measure pattern almost instantly

moving horizon estimation for discrete time linear systems with binary sensors algorithms and stability results abstract the paper addresses state estimation for linear discrete time systems with binary threshold measurements a moving horizon estimation mhe approach is followed and different estimators characterized by two different choices of the cost function to be minimized and or by the possible inclusion of constraints are proposed specifically the cost function is either quadratic when only the information pertaining to the threshold crossing instants is exploited or piece wise quadratic when all the available binary measurements are taken into account stability results are provided for the proposed mhe algorithms in the presence of unknown but bounded disturbances and measurement noise performance of the proposed techniques is also assessed by means of simulation examples

combinatorial benders cuts for the strip packing problem we study the strip packing problem in which a set of two dimensional rectangular items has to be packed in a rectangular strip of fixed width and infinite height with the aim of minimizing the height used the problem is important because it models a large number of real world applications including cutting operations where stocks of materials such as paper or wood come in large rolls and have to be cut with minimum waste scheduling problems in which tasks require a contiguous subset of identical resources and container loading problems arising in the transportation of items that cannot be stacked one over the other the strip packing problem has been attacked in the literature with several heuristic and exact algorithms nevertheless benchmark instances of small size remain unsolved to proven optimality in this paper we propose a new exact method that solves a large number of the open benchmark instances within a limited computational effort our method is based on a benders decomposition in whi

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

niching particle swarm optimization with local search for multi modal optimization multimodal optimization is still one of the most challenging tasks for evolutionary computation in recent years many evolutionary multi modal optimization algorithms have been developed all these algorithms must tackle two issues in order to successfully solve a multi modal problem how to identify multiple global local optima and how to maintain the identified optima till the end of the search for most of the multi modal optimization algorithms the fine local search capabilities are not effective if the required accuracy is high these algorithms fail to find the desired optima even after converging near them to overcome this problem this paper integrates a novel local search technique with some existing pso based multimodal optimization algorithms to enhance their local search ability the algorithms are tested on 14 commonly used multi modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations