a derivative free variant called dfsa of dekkers and aarts continuous simulated annealing algorithm abstract we propose a derivative free implementation of dekkers and aarts continuous simulated annealing sa essential in dfsa is the random direction local technique that we introduce we study the convergence properties of the resulting algorithm and test its performance on a set of 50 problems numerical results are presented which show the robustness of the algorithm numerical comparisons with sa two derivative free simulated annealing algorithms and three population based global optimization algorithms show that the derivative free sa dfsa offers a reasonable alternative to some recent global optimization algorithms especially for problems requiring a direct search type algorithm

om3 ordered maxitive minitive and modular aggregation operators axiomatic and probabilistic properties in an arity monotonic setting the recently introduced om3 aggregation operators fulfill three appealing properties they are simultaneously minitive maxitive and modular among the instances of om3 operators we find e g owmax and owmin operators the famous hirsch h index and all its natural generalizations in this paper the basic axiomatic and probabilistic properties of extended i e in an arity dependent setting om3 aggregation operators are studied we illustrate the di culties one is inevitably faced with when trying to combine the quality and quantity of numeric items into a single number the discussion on such aggregation methods is particularly important in the information resources producers assessment problem which aims to reduce the negative e ects of information overload it turns out that the hirsch like indices of impact do not fulfill a set of very important properties which puts the sensibility of their practical usage into question moreover thanks to the probabilistic analysis of the operators in an i i d model we may better understand the relationship between the aggregated items quality and their producers productivity

bare bones differential evolution the barebones differential evolution bbde is a new almost parameter free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution differential evolution is used to mutate for each particle the attractor associated with that particle defined as a weighted average of its personal and neighborhood best positions the performance of the proposed approach is investigated and compared with differential evolution a von neumann particle swarm optimizer and a barebones particle swarm optimizer the experiments conducted show that the bbde provides excellent results with the added advantage of little almost no parameter tuning moreover the performance of the barebones differential evolution using the ring and von neumann neighborhood topologies is investigated finally the application of the bbde to the real world problem of unsupervised image classification is investigated experimental results show that the proposed approach performs very well compared to other state of the art clustering algorithms in all measured criteria

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

a robust adaptive method for singularly perturbed convection diffusion problem with two small parameters abstract numerical treatment for a one dimensional two parameter singularly perturbed convection diffusion equation is proposed and analyzed the upwind finite difference method is applied to the problem maximum norm a posteriori error estimates are obtained and used to establish an adaptive algorithm the uniform convergence of the new algorithm is proved under feasible conditions of two parameters numerical experiments are given to confirm the analytical results

capacities and overlap indexes with an application in fuzzy rule based classification systems in this work we introduce a method for constructing capacities using overlap indexes between the fuzzy sets which are generated from the inputs of the considered problem we also use these capacities to aggregate information by means of the choquet integral in a fuzzy rule based classifier we observe that with these capacities the obtained results are better than those obtained with other measures

owa generation function and some adjustment methods for owa operators with application we propose the concept of ordered weighted averaging owa generation function with some properties illustrative examples and usages a number of new properties of owa operators and relations between some well known owa operators are proposed and proved using owa generation functions we discuss some orness andness adjustment methods for a predetermined owa operator a practical application about investment prediction problem is given with detailed illustration and analysis which can show the advantages of the adjustment methods proposed in this paper in particular the concept consistent adjustment matrix is proposed and the interests and advantages of it can be shown by its special properties

structural bias in population based algorithms challenging optimisation problems are abundant in all areas of science and industry since the 1950s scientists have responded to this by developing ever diversifying families of black box optimisation algorithms the latter are designed to be able to address any optimisation problem requiring only that the quality of any candidate solution can be calculated via a fitness function specific to the problem for such algorithms to be successful at least three properties are required i an effective informed sampling strategy that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates ii mechanisms to ensure efficiency so that for example the same candidates are not repeatedly visited and iii the absence of structural bias which if present would predispose the algorithm towards limiting its search to specific regions of the solution space the first two of these properties have been extensively investigated however the third is little understood and rarely explored in this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias in particular we state and prove a theorem concerning the dynamics of population variance in the case of real valued search spaces and a flat fitness landscape this reveals how structural bias can arise and manifest as non uniform clustering of the population over time critically theory predicts that structural bias is exacerbated with independently increasing population size and increasing problem difficulty these predictions supported by our empirical analyses reveal two previously unrecognised aspects of structural bias that would seem vital for algorithm designers and practitioners respectively i increasing the population size though ostensibly promoting diversity will magnify any inherent structural bias and ii the effects of structural bias are more apparent when faced with many classes of difficult problems our theoretical result also contributes to the exploitation exploration conundrum in optimisation algorithm design by suggesting that two commonly used approaches to enhancing exploration increasing the population size and increasing the disruptiveness of search operators have quite distinct implications in terms of structural bias

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

a modified particle swarm optimization via particle visual modeling analysis a particle is treated as a whole individual in all researches on particle swarm optimization pso currently these are not concerned with the information of every particle s dimensional vector a visual modeling method describing particle s dimensional vector behavior is presented in this paper based on the analysis of visual modeling the reason for premature convergence and diversity loss in pso is explained and a new modified algorithm is proposed to ensure the rational flight of every particle s dimensional component meanwhile two parameters of particle distribution degree and particle dimension distance are introduced into the proposed algorithm in order to avoid premature convergence simulation results of the new pso algorithm show that it has a better ability of finding the global optimum and still keeps a rapid convergence as with the standard pso

a simulated annealing driven multi start algorithm for bound constrained global optimization a derivative free simulated annealing driven multi start algorithm for continuous global optimization is presented we first propose a trial point generation scheme in continuous simulated annealing which eliminates the need for the gradient based trial point generation we then suitably embed the multi start procedure within the simulated annealing algorithm we modify the derivative free pattern search method and use it as the local search in the multi start procedure we study the convergence properties of the algorithm and test its performance on a set of 50 problems numerical results are presented which show the robustness of the algorithm numerical comparisons with a gradient based simulated annealing algorithm and three population based global optimization algorithms show that the new algorithm could offer a reasonable alternative to many currently available global optimization algorithms specially for problems requiring direct search type algorithm

maximum norm a posteriori error estimate for a 2d singularly perturbed semilinear reaction diffusion problem a singularly perturbed semilinear reaction diffusion equation posed in the unit square is discretized on arbitrary nonuniform tensor product meshes we establish a second order maximum norm a posteriori error estimate that holds true uniformly in the small diffusion parameter no mesh aspect ratio assumption is made numerical results are presented that support our theoretical estimate

maximum norm a posteriori error estimates for a one dimensional convection diffusion problem a singularly perturbed quasi linear two point boundary value problem with an exponential boundary layer is discretized on arbitrary nonuniform meshes using first and second order difference schemes including upwind schemes we give first and second order maximum norm a posteriori error estimates that are based on difference derivatives of the numerical solution and hold true uniformly in the small parameter numerical experiments support the theoretical results

enhancing particle swarm optimization using generalized opposition based learning particle swarm optimization pso has been shown to yield good performance for solving various optimization problems however it tends to suffer from premature convergence when solving complex problems this paper presents an enhanced pso algorithm called gopso which employs generalized opposition based learning gobl and cauchy mutation to overcome this problem gobl can provide a faster convergence and the cauchy mutation with a long tail helps trapped particles escape from local optima the proposed approach uses a similar scheme as opposition based differential evolution ode with opposition based population initialization and generation jumping using gobl experiments are conducted on a comprehensive set of benchmark functions including rotated multimodal problems and shifted large scale problems the results show that gopso obtains promising performance on a majority of the test problems

some quantities represented by the choquet integral this paper shows that the choquet integral can represent some useful quantities such as supremum infimum essential supremum essential infimum mean median quantile 100 percentile and l estimators including trimmed mean

generalized ordered modular averaging operator and its application to group decision making in this paper we propose a new class of operators called the ordered quasi modular averaging oqma operators based on the ordered modular averaging oma operators it is shown that the oqma operators are monotonic bounded idempotent commutative and quasi comonotone modular moreover we introduce the generalized ordered modular averaging goma operator which is a special case of the oqma operator some special cases of the goma operator are discussed an orness measure to reflect the or like degree of the goma operator is proposed we further extend the goma operator to the generalized ordered hybrid modular gohm operator which focuses not only on the degree of importance with respect to input arguments but also their serial positions finally a new method based on the gohm operator for multi attribute group decision making is presented and a numerical example shows that the developed approach is feasible

generalizations of owa operators owa operators can be seen as symmetrized weighted arithmetic means as choquet integrals with respect to symmetric measures or as comonotone additive functionals following these three different looks on owas we discuss several already known generalizations of owa operators including gowa iowa oma operators as well as we propose new types of such generalizations

generalized cf1f2 integrals from choquet like aggregation to ordered directionally monotone functions abstract this paper introduces the theoretical framework for a generalization of c f 1 f 2 integrals a family of choquet like integrals used successfully in the aggregation process of the fuzzy reasoning mechanisms of fuzzy rule based classification systems the proposed generalization called by g c f 1 f 2 integrals is based on the so called pseudo pre aggregation function pairs f 1 f 2 which are pairs of fusion functions satisfying a minimal set of requirements in order to guarantee that the g c f 1 f 2 integrals to be either an aggregation function or just an ordered directionally increasing function satisfying the appropriate boundary conditions we propose a dimension reduction of the input space in order to deal with repeated elements in the input avoiding ambiguities in the definition of g c f 1 f 2 integrals we study several properties of g c f 1 f 2 integrals considering different constraints for the functions f 1 and f 2 and state under which conditions g c f 1 f 2 integrals present or not averaging behaviors several examples of g c f 1 f 2 integrals are presented considering different pseudo pre aggregation function pairs defined on e g t norms overlap functions copulas that are neither t norms nor overlap functions and other functions that are not even pre aggregation functions

multi objective particle swarm optimization with time variant inertia and acceleration coefficients in this article we describe a novel particle swarm optimization pso approach to multi objective optimization moo called time variant multi objective particle swarm optimization tv mopso tv mopso is made adaptive in nature by allowing its vital parameters viz inertia weight and acceleration coefficients to change with iterations this adaptiveness helps the algorithm to explore the search space more efficiently a new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front tv mopso has been compared with some recently developed multi objective pso techniques and evolutionary algorithms for 11 function optimization problems using different performance measures

time series smoothing and owa aggregation in this paper an overview of the time series smoothing problem is provided and the role of data aggregation is described using a weighted average of the past observations two important features associated with this smoothing process are introduced one is the average age of the data and other is the expected variance it is noted how both of these are defined in terms of the associated weights we show the fundamental conflict between trying to keep the variance small while simultaneously using the freshest data we study the moving average and exponential smoothing methods focusing on the aggregation aspect of the data smoothing problem allows us to draw upon the work done with the ordered weighted averaging aggregation operators to suggest new methods for developing smoothing techniques a new class of smoothing operators based on the use of linearly decaying weights is introduced and is shown to have some better features than either exponential smoothing or the moving average other classes of smoothing operators are introduced we introduce the idea of an operational estimate that involves adjusting a smoothed value by other considerations

inter particle communication and search dynamics of lbest particle swarm optimizers an analysis particle swarm optimization pso is arguably one of the most popular nature inspired algorithms for real parameter optimization at present the existing theoretical research on pso focuses on the issues like stability convergence and explosion of the swarm however all of them are based on the gbest global best communication topology which usually is susceptible to false or premature convergence over multi modal fitness landscapes the present standard pso spso 2007 uses an lbest local best topology where a particle is stochastically attracted not towards the best position found in the entire swarm but towards the best position found by any particle in its topological neighborhood this article presents a first step towards a probabilistic analysis of the particle interaction and information exchange in an lbest pso with variable random neighborhood topology as found in spso 2007 it addresses issues like the distribution of particles over neighborhoods the probability distributions of the social and cognitive terms in lbest model and the explorative power of the lbest pso it also presents a state space model of the lbest pso and draws important conclusions regarding the stability and convergence of the particle dynamics in the light of control theory

measure based aggregation operators in analogy to the representation of the standard probabilistic average as an expected value of a random variable a geometric approach to aggregation is proposed generalizing imaoka s integral based on copulas several properties of such aggregation operators are investigated and the relationship with distinguished classes of aggregation operators is discussed

improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem 1 evolutionary algorithms eas have been applied successfully to many water resource problems such as system design management decision formulation and model calibration the performance of an ea with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation exploration trade off to iteratively find solutions of an increasing quality for a given problem different algorithms are observed to produce a variety of different final performances but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm s searching behavior in both the objective and decision space to arrive at this final performance this paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms a particular type of ea for the optimal water distribution system design problem which is a classical np hard problem in civil engineering using the proposed metrics behavior is characterized in terms of three different attributes 1 the effectiveness of the search in improving its solution quality and entering into optimal or near optimal regions of the search space 2 the extent to which the algorithm explores as it converges to solutions and 3 the searching behavior with respect to the feasible and infeasible regions a range of case studies is considered where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems the results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm s searching behavior

a modified binary particle swarm optimization for knapsack problems abstract the knapsack problems kps are classical np hard problems in operations research having a number of engineering applications several traditional as well as population based search algorithms are available in literature for the solution of these problems in this paper a new modified binary particle swarm optimization mbpso algorithm is proposed for solving kps particularly 0 1 knapsack problem kp and multidimensional knapsack problem mkp compared to the basic binary particle swarm optimization bpso this improved algorithm introduces a new probability function which maintains the diversity in the swarm and makes it more explorative effective and efficient in solving kps mbpso is tested through computational experiments over benchmark problems and the results are compared with those of bpso and a relatively recent modified version of bpso namely genotype phenotype modified binary particle swarm optimization gpmbpso to validate our idea and demonstrate the efficiency of the proposed algorithm for kps experiments are carried out with various data instances of kp and mkp and the results are compared with those of bpso and gpmbpso

a numerical study of some modified differential evolution algorithms abstract modifications in mutation and localization in acceptance rule are suggested to the differential evolution algorithm for global optimization numerical experiments indicate that the resulting algorithms are considerably better than the original differential evolution algorithm therefore they offer a reasonable alternative to many currently available stochastic algorithms especially for problems requiring direct search type methods numerical study is carried out using a set of 50 test problems many of which are inspired by practical applications

a note on cc integral abstract recently choquet like copula based aggregation functions were introduced and discussed by lucca et al afterwards cmin integral i e a choquet like minimum based operator was examined by dimuro et al we show the link of these aggregation functions with universal integrals of klement et al in particular cmin integral is shown to be an alternative approach to the sugeno integral assuming symmetry an axiomatic characterization of the discussed aggregation functions is given and their coincidence with ordered modular averages in short oma operators is shown

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

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

global optimization using a genetic algorithm with hierarchically structured population this paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems the population has individuals distributed in several overlapping clusters each one with a leader and a variable number of support individuals the hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree computational tests evaluate different population structures population sizes and crossover operators for better algorithm performance a set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature namely two genetic algorithms a simulated annealing a differential evolution and a particle swarm optimization the results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons the number of function evaluations and rate of success the method also has a superior performance if the number of problems solved is taken into account

reprint of on convergence analysis of particle swarm optimization algorithm abstract particle swarm optimization pso a population based stochastic optimization algorithm has been successfully used to solve many complicated optimization problems however analysis on algorithm convergence is still inadequate till now in this paper the martingale theory is applied to analyze the convergence of the standard pso spso firstly the swarm state sequence is defined and its markov properties are examined according to the theory of spso two closed sets the optimal particle state set and optimal swarm state set are then obtained afterwards a supermartingale is derived as the evolutionary sequence of particle swarm with the best fitness value finally the spso convergence analysis is carried out in terms of the supermartingale convergence theorem our results show that spso reaches the global optimum in probability moreover the analysis on spso proves that the quantum behaved particle swarm optimization qpso is also a global convergence algorithm the proof of the spso convergence in this work is new simple and more effective without specific implementation

the differential ant stigmergy algorithm ant colony optimization aco is a popular swarm intelligence scheme known for its efficiency in solving combinatorial optimization problems however despite some extensions of this approach to continuous optimization high dimensional problems remain a challenge for aco this paper presents an aco based algorithm for numerical optimization capable of solving high dimensional real parameter optimization problems the algorithm called the differential ant stigmergy algorithm dasa transforms a real parameter optimization problem into a graph search problem the parameters differences assigned to the graph vertices are used to navigate through the search space we compare the algorithm results with the results of previous studies on recent benchmark functions and show that the dasa is a competitive continuous optimization algorithm that solves high dimensional problems effectively and efficiently

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

multi operator based biogeography based optimization with mutation for global numerical optimization biogeography based optimization bbo is a new evolutionary optimization based on the science of biogeography for global optimization we propose two extensions to bbo first we propose a new migration operation based multi parent crossover called multi parent migration model which is a generalization of the standard bbo migration operator the new migration model can satisfy a balance of exploration and exploitation second the gaussian mutation operator is integrated into multi operator biogeography based optimization mobbo to enhance its exploration ability and to improve the diversity of population experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities simulation results and comparisons demonstrate the proposed mobbo algorithm based multi parent crossover model is better or at least comparable to the bbo pbbo and evolutionary algorithms from literature when considering the quality of the solutions obtained

chaotic catfish particle swarm optimization for solving global numerical optimization problems abstract chaotic catfish particle swarm optimization c catfishpso is a novel optimization algorithm proposed in this paper c catfishpso introduces chaotic maps into catfish particle swarm optimization catfishpso which increase the search capability of catfishpso via the chaos approach simple catfishpso relies on the incorporation of catfish particles into particle swarm optimization pso the introduced catfish particles improve the performance of pso considerably unlike other ordinary particles the catfish particles initialize a new search from extreme points of the search space when the gbest fitness value global optimum at each iteration has not changed for a certain number of consecutive iterations this results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space and accelerating the search the introduced chaotic maps strengthen the solution quality of pso and catfishpso significantly the resulting improved pso and catfishpso are called chaotic pso c pso and chaotic catfishpso c catfishpso respectively pso c pso catfishpso c catfishpso as well as other advanced pso procedures from the literature were extensively compared on several benchmark test functions statistical analysis of the experimental results indicate that the performance of c catfishpso is better than the performance of pso c pso catfishpso and that c catfishpso is also superior to advanced pso methods from the literature

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

solving 0 1 knapsack problems based on amoeboid organism algorithm the 0 1 knapsack problem is an open issue in discrete optimization problems which plays an important role in real applications in this paper a new bio inspired model is proposed to solve this problem the proposed method has three main steps first the 0 1 knapsack problem is converted into a directed graph by the network converting algorithm then for the purpose of using the amoeboid organism model the longest path problem is transformed into the shortest path problem finally the shortest path problem can be well handled by the amoeboid organism algorithm numerical examples are given to illustrate the efficiency of the proposed model

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

using stress functions to obtain owa operators the ordered weighted averaging operator attempts to provide a user friendly class of mean type aggregation operators by allowing a user to easily and naturally go from a mental model of a desired type of aggregation to its formal realization our objective in this paper is to support this agenda by providing a new methodology to help a user describe the type of aggregation they desire using the idea of a stress function with this stress function a user can stress which argument values they want to give more weight in the aggregation an important feature of this stress function is that it is only required to be nonnegative function on the unit interval this allows a user to completely focus on the issue of where to put the stress in the aggregation without having to consider satisfaction of any other requirements

fuzzy integral in multicriteria decision making abstract this paper presents a synthesis on the use of fuzzy integral as an aggregation operator in multicriteria decision making definitions essential properties are given and compared to those of usual aggregation operators a quick survey on applications is given

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

a robust adaptive method for a quasi linear one dimensional convection diffusion problem a quasi linear conservative convection diffusion two point boundary value problem is considered to solve it numerically an upwind finite difference scheme is applied the mesh used has a fixed number n 1 of nodes and is initially uniform but its nodes are moved adaptively using a simple algorithm of de boor based on equidistribution of the arc length of the current computed piecewise linear solution it is proved for the first time that a mesh exists that equidistributes the arc length along the polygonal solution curve and that the corresponding computed solution is first order accurate uniformly in varepsilon where varepsilon is the diffusion coefficient in the case when the boundary value problem is linear if n is sufficiently large independently of varepsilon it is shown that after o rm ln 1 varepsilon rm ln n iterations of the algorithm the piecewise linear interpolant of the computed solution achieves first order accuracy in the l infty 0 1 norm uniformly in varepsilon numerical experiments are presented that support our theoretical results

topology selection for particle swarm optimization particle swarm optimization pso uses a social topology for particles to share information among neighbors during optimization a large number of existing literatures have shown that the topology affects the performance of pso and an optimal topology is problem dependent but currently there is a lack of study on this issue in this paper we first analyze a class of deterministic regular topologies with regard to what affect the optimality of algorithmic parameters e g the number of particles and the topological degree so as to provide a guide to topology selections for pso both theoretical analysis and numerical experiments are performed and reported in detail the theoretical analysis reveals that the optimality of algorithmic parameters is dependent on the computational budget available in particular the optimal number of particles increases unstrictly as the computational budget increases while for any fixed number of particles the optimal degree decreases unstrictly as computational budget increases the only condition is that the computational budget cannot exceed a constant measuring the hardness of the benchmark function set with a total of 198 regular topologies and 9 different numbers of particles tested on 90 benchmark functions using a recently reported data profiling technique numerical experiments verify the theoretical derivations based on these results two formulas are developed to help choose optimal topology parameters for increased ease and applicability of pso to real world problems

the ordered modular averages ordered modular averages omas generalize ordered weighted averages operators with the replacement of the additivity property by the modularity omas can be seen as a symmetrized idempotent modular aggregation functions characterized by comonotone modularity each oma can be seen also as a copula based integral with respect to a symmetric capacity special omas are characterized such as associative operators operators with neutral element or operators with an annihilator some construction methods for omas are also discussed

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

on fast and accurate block based motion estimation algorithms using particle swarm optimization both fast and accurate block matching algorithms are critical to efficient compression of video frames using motion estimation and compensation while the particle swarm optimization approach holds the promise of alleviating the local optima problem suffered typically by existing very fast block matching methods motion estimation algorithms based on particle swarm optimization in the literature appear to be either much slower than some leading fast block matching methods for a given accuracy of motion estimation or less accurate for a given computational complexity in this paper we show that the conventional particle swarm optimization approach which was originally designed to solve general optimization problems where fast convergence of the algorithm might not be a primary concern could be modified appropriately so that it could provide accurate motion estimation with very low computational cost in the specific context of video motion estimation to this end we proposed a new block matching algorithm based on a set of strategies adapted from the standard particle swarm optimization approach extensive simulations showed that the proposed method could achieve significant improvements over leading fast block matching methods including the diamond search and the cross diamond search methods in terms of both estimation accuracy and computational cost in particular the proposed method based on particle swarm optimization is not only much faster but also remarkably more accurate about 2db higher in terms of the peak signal to noise ratio than the competing methods on video sequences with large motion

a study of particle swarm optimization particle trajectories particle swarm optimization pso has shown to be an efficient robust and simple optimization algorithm most of the pso studies are empirical with only a few theoretical analyses that concentrate on understanding particle trajectories these theoretical studies concentrate mainly on simplified pso systems this paper overviews current theoretical studies and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term the paper also provides a formal proof that each particle converges to a stable point an empirical analysis of multi dimensional stochastic particles is also presented experimental results are provided to support the conclusions drawn from the theoretical findings

the owa aggregation with uncertain descriptions on weights and input arguments since the ordered weighted averaging owa operator was introduced by yager to provide a method for aggregating multiple inputs that lie between the max and min operators much research that deals with uncertain information on input arguments instead of numerical values has been conducted due to the fact that information provided by human beings is in fact often vague and imprecise in this paper the owa operators with uncertain weights such as interval or weak ordinal descriptions coupled with interval input arguments are presented in order to identify the superior course of action among a multitude of courses of action with the help of relaxation of information representation a burden of information specification imposed on decision maker can be to some extent relieved and thus with regard to the parameters we can obtain a less specific expression that renders human judgments readily available to perform the owa aggregation we take into account the strength of preference based on a probabilistic measure and present a way of ordering the input arguments specified by interval numbers as well as two heuristic algorithms to allocate uncertain weights to the ordered input arguments for prioritizing the courses of action

a universal integral as common frame for choquet and sugeno integral the choquet and the sugeno integral provide a useful tool in many problems in engineering and social choice where the aggregation of data is required however their applicability is restricted because of the special operations used in the construction of these integrals therefore we provide a concept of integrals generalizing both the choquet and the sugeno case for functions with values in the nonnegative real numbers universal integrals are introduced and investigated which can be defined on arbitrary measurable spaces and for arbitrary monotone measures for a fixed pseudo multiplication on the nonnegative real numbers the smallest and the greatest universal integrals are given finally another construction method for obtaining universal integrals is introduced and the restriction to the unit interval i e to fuzzy integrals is considered

improved particle swarm algorithms for global optimization particle swarm optimization algorithm has recently gained much attention in the global optimization research community as a result a few variants of the algorithm have been suggested in this paper we study the efficiency and robustness of a number of particle swarm optimization algorithms and identify the cause for their slow convergence we then propose some modifications in the position update rule of particle swarm optimization algorithm in order to make the convergence faster these modifications result in two new versions of the particle swarm optimization algorithm a numerical study is carried out using a set of 54 test problems some of which are inspired by practical applications results show that the new algorithms are much more robust and efficient than some existing particle swarm optimization algorithms a comparison of the new algorithms with the differential evolution algorithm is also made

memetic binary particle swarm optimization for discrete optimization problems in recent decades many researchers have been interested in algorithms inspired by the observation of natural phenomena to solve optimization problems among them meta heuristic algorithms have been extensively applied in continuous real and discrete binary search spaces such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities in this study a memetic binary particle swarm optimization bpso scheme is introduced based on hybrid local and global searches in bpso the algorithm binary hybrid topology particle swarm optimization bhtpso is used to solve the optimization problems in the binary search spaces in addition a variant of the proposed algorithm binary hybrid topology particle swarm optimization quadratic interpolation bhtpso qi is proposed to enhance the global searching capability these algorithms are tested on two set of problems in the binary search space several nonlinear high dimension functions and benchmarks for the 0 1 multidimensional knapsack problem mkp are employed to evaluate their performances their results are compared with some well known modified binary pso and binary gravitational search algorithm bgsa the experimental results showed that the proposed methods improve the performance of bpso in terms of convergence speed and solution accuracy

axiomatizations of lovasz extensions of pseudo boolean functions three important properties in aggregation theory are investigated namely horizontal min additivity horizontal max additivity and comonotonic additivity which are defined by certain relaxations of the cauchy functional equation in several variables we show that these properties are equivalent and we completely describe the functions characterized by them by adding some regularity conditions these functions coincide with the lovasz extensions vanishing at the origin which subsume the discrete choquet integrals we also propose a simultaneous generalization of horizontal min additivity and horizontal max additivity called horizontal median additivity and we describe the corresponding function class additional conditions then reduce this class to that of symmetric lovasz extensions which includes the discrete symmetric choquet integrals

an ant colony optimization approach for binary knapsack problem under fuzziness in this paper we represent a novel ant colony optimization algorithm to solve binary knapsack problem in the proposed algorithm for n objects n candidate groups are created and each candidate group has exactly m values for m ants as 0 or 1 for each candidate value in each group a pheromone is initialized by the value between 0 1 and 0 9 and each ant selects a candidate value from each group therefore the binary solution is generated by each ant by selecting a value from each group in each generation pheromone update and evaporation is done during the execution of algorithm after certain number of generation the best solution is stored as a temporary population after that crossover and mutation is performed between the solution generated by ants we consider profit and weight are fuzzy in nature and taken as trapezoidal fuzzy number fuzzy possibility and necessity approaches are used to obtain optimal decision by the proposed ant colony algorithm computational experiments with different set of data are given in support of the proposed approach

reservoir characterization and inversion uncertainty via a family of particle swarm optimizers history matching provides to reservoir engineers an improved spatial distribution of physical properties to be used in forecasting the reservoir response for field management the ill posed character of the history matching problem yields nonuniqueness and numerical instabilities that increase with the reservoir complexity these features might cause local optimization methods to provide unpredictable results not being able to discriminate among the multiple models that fit the observed data production history also the high dimensionality of the inverse problem impedes estimation of uncertainties using classical markov chain monte carlo methods we attenuated the ill conditioned character of this history matching inverse problem by reducing the model complexity using a spatial principal component basis and by combining as observables flow production measurements and time lapse seismic crosswell tomographic images additionally the inverse problem was solved in a stochastic framework for this purpose

a hybrid binary particle swarm optimization for the obnoxious p median problem abstract the obnoxious p median problem can be formulated as a constrained binary linear program it is np hard and has a lot of real world applications in this paper a hybrid binary particle swarm optimization is proposed to solve the obnoxious p median problem a new position updating rule is presented to inherit the good structure of previous high quality solutions furthermore two tabu based mutation operators are used to avoid the premature convergence and guide the search to a promising area a greedy repair procedure is developed to repair infeasible solutions in addition an iterated greedy local search procedure is utilized to enhance the exploitation ability extensive experiments are done on a set of 72 benchmark instances from the literature experimental results and comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm in particular the proposed algorithm finds new best solutions for 15 instances compared with existing algorithms the proposed algorithm is able to find better average objective function value in a short average computing time

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

new upper bounds and exact methods for the knapsack sharing problem a new upper bound is proposed for the knapsack sharing problem a new dichotomous search based exact algorithm is proposed the new algorithm outperforms a most recent exact algorithm of the literature and the cplex solver v 12 in this paper we study the knapsack sharing problem ksp which is a variant of the well known np hard knapsack problem we investigate the use of a dichotomous search based exact method for solving the ksp such a method is based on decomposing the original problem into a series of minimizing and maximizing knapsack problems where each of them is embedded into a dichotomous search bounded by both lower and upper bounds throughout the interval search we introduce new upper bounds and incremental valid lower bounds one of these upper bounds can be viewed as an extended of dantzig upper bound whereas a series of valid lower bounds are obtained when solving a subset of knapsack problems finally the proposed algorithm is evaluated on a set of problem instances of the literature and other new harder ones the provided results are compared to those reached by the cplex solver and a more recent exact algorithm of the literature the computational part shows the dominance of the new exact method

weighted fuzzy pattern matching the fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared in a matching process this technique has proved very useful for implementing patterns of approximate reasoning in expert system inference engines and for designing retrieval systems capable of managing incomplete and fuzzy information data bases and vague queries in this paper the fuzzy pattern matching procedure is improved by introducing weights assessing the relative importance of atoms in the pattern matching indices are obtained using weighted versions of the minimum and maximum operations of fuzzy set theory this approach is extended to the case of variable weights and is related to aggregation schemes proposed by yager when modeling soft quantified statements such as most of the criteria must be met a notion of soft partial matching is thus modelled

on the relationship between symmetric maxitive minitive and modular aggregation operators in this paper the relationship between symmetric minitive maxitive and modular aggregation operators is considered it is shown that the intersection between any two of the three discussed classes is the same moreover the intersection is explicitly characterized it turns out that the intersection contains families of aggregation operators such as owmax owmin and many generalizations of the widely known hirsch s h index often applied in scientific quality control

quantum inspired space search algorithm qssa for global numerical optimization abstract this work presents the evolutionary quantum inspired space search algorithm qssa for solving numerical optimization problems in the proposed algorithm the feasible solution space is decomposed into regions in terms of quantum representation as the search progresses from one generation to the next the quantum bits evolve gradually to increase the probability of selecting the regions that render good fitness values through the inherent probabilistic mechanism the qssa initially behaves as a global search algorithm and gradually evolves into a local search algorithm yielding a good balance between exploration and exploitation to prevent a premature convergence and to speed up the overall search speed an overlapping strategy is also proposed the qssa is applied to a series of numerical optimization problems the experiments show that the results obtained by the qssa are quite competitive compared to those obtained using state of the art ipop cma es and qea

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 new heuristic approach for non convex optimization problems in this work a new optimization method called the heuristic kalman algorithm hka is presented this new algorithm is proposed as an alternative approach for solving continuous non convex optimization problems the principle of hka is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum a specific procedure based on the kalman estimator was developed to improve the quality of the estimate obtained through the measurement process the main advantage of hka compared to other metaheuristics lies in the small number of parameters that need to be set by the user further it is shown that hka converges almost surely to a near optimal solution the efficiency of hka was evaluated in detail using several non convex test problems both in the unconstrained and constrained cases the results were then compared to those obtained via other metaheuristics the numerical experiments show that hka is a promising approach for solving non convex optimization problems particularly in terms of computation time and success ratio

discrete particle swarm optimization for high order graph matching high order graph matching aims at establishing correspondences between two sets of feature points using high order constraints it is usually formulated as an np hard problem of maximizing an objective function this paper introduces a discrete particle swarm optimization algorithm for resolving high order graph matching problems which incorporates several re defined operations a problem specific initialization method based on heuristic information and a problem specific local search procedure the proposed algorithm is evaluated on both synthetic and real world datasets its outstanding performance is validated in comparison with three state of the art approaches