an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search recently more research works are focused on multi objective particle swarm optimization algorithm mopso due to its ability of global and local search for solving multi objective optimization problems moops however most of existing mopsos cannot achieve satisfactory results in solution quality this paper proposes an efficient hybrid multi objective particle swarm optimization with a multi objective dichotomy line search mols named mols mopso to deal with such problem mols mopso combines an effective particle updating strategy with the local search of mols the effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm the mols is periodically activated for fast local search to converge toward the pareto front the exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search so as to ensure sufficient diversity and well distribution amongst the solutions of the non dominated fronts while retaining at the same time the convergence to the pareto optimal front comparing mols mopso with various state of the art multi objective optimization algorithms developed recently the comparative study shows the effectiveness of mols mopso which not only assures a better convergence to the pareto frontier but also illustrates a good diversity and distribution of solutions

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

the fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain abstract the problem of vehicle routing when demand at the nodes is uncertain is considered the quantities to be picked up at the nodes are assumed to be only approximately known a network with one depot from which vehicles depart and to which they return after completing their service is considered the paper develops a model to design vehicle routing when demand at the nodes is uncertain the model is based on the heuristic sweeping algorithm the rules of fuzzy arithmetic and fuzzy logic

polynomial modeling for time varying systems based on a particle swarm optimization algorithm in this paper an effective particle swarm optimization pso is proposed for polynomial models for time varying systems the basic operations of the proposed pso are similar to those of the classical pso except that elements of particles represent arithmetic operations and variables of time varying models the performance of the proposed pso is evaluated by polynomial modeling based on various sets of time invariant and time varying data results of polynomial modeling in time varying systems show that the proposed pso outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming gp and dynamic gp an analysis of the diversity of individuals of populations in the proposed pso and gp reveals why the proposed pso obtains better results than those obtained by gp

on the computational efficiency of multiple objective metaheuristics the knapsack problem case study abstract the paper describes a computational experiment which goal is to evaluate computational efficiency of three multiple objective evolutionary metaheuristics on the multiple objective multiple constraints knapsack problem the relative efficiency of the multiple objective algorithms is evaluated with respect to a single objective evolutionary algorithm ea we use a methodology that allows consistent evaluation of the quality of approximately pareto optimal solutions generated by both multiple and single objective metaheuristics then we compare computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods the results indicate that computational efficiency of multiple objective eas deteriorates with the growth of the number of objectives furthermore significant differences in the performance of the three algorithms are observed

an improved bl algorithm for genetic algorithm of the orthogonal packing of rectangles this paper proposes an improved bl algorithm for genetic algorithm of the orthogonal packing of rectangles some improvements on the fitness function of genetic algorithm for the orthogonal packing of rectangles are also suggested solutions of two numerical examples show the effectiveness of these improvements

a co evolutionary multi objective optimization algorithm based on direction vectors most real world multi objective problems mops have a complicated solution space facing such problems a direction vectors based co evolutionary multi objective optimization algorithm dvcmoa that introduces the decomposition idea from moea d to co evolutionary algorithms is proposed in this paper it is novel in the sense that dvcmoa applies the concept of direction vectors to co evolutionary algorithms dvcmoa first divides the entire population into several subpopulations on the basis of the initial direction vectors in the objective space then it solves mops through the co evolutionary interaction among the subpopulations in which individuals are classified according to their direction vectors finally it explores the less developed regions to maintain the relatively uniform distribution of the solution space in this way dvcmoa has advantages in convergence diversity and uniform distribution of the non dominated solution set which are explained through comparison with other state of the art multi objective optimization evolutionary algorithms moeas in this paper dvcmoa is shown to be effective on 6 multi objective 0 1 knapsack problems

variable neighborhood search principles and applications abstract systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and effective metaheuristic for combinatorial and global optimization called variable neighborhood search vns we present a basic scheme for this purpose which can easily be implemented using any local search algorithm as a subroutine its effectiveness is illustrated by solving several classical combinatorial or global optimization problems moreover several extensions are proposed for solving large problem instances using vns within the successive approximation method yields a two level vns called variable neighborhood decomposition search vnds modifying the basic scheme to explore easily valleys far from the incumbent solution yields an efficient skewed vns svns heuristic finally we show how to stabilize column generation algorithms with help of vns and discuss various ways to use vns in graph theory i e to suggest disprove or give hints on how to prove conjectures an area where metaheuristics do not appear to have been applied before

heuristic algorithms for the three dimensional bin packing problem abstract the three dimensional bin packing problem 3bp consists of allocating without overlapping a given set of three dimensional rectangular items to the minimum number of three dimensional identical finite bins the problem is np hard in the strong sense and finds many industrial applications we introduce a tabu search framework exploiting a new constructive heuristic for the evaluation of the neighborhood extensive computational results on standard benchmark instances show the effectiveness of the approach with respect to exact and heuristic algorithms from the literature

on k3 double planes covering enriques surfaces the moduli space of nodal enriques surfaces is irreducible of dimension 9 a nodal enriques surface is shown to be the quotient of a double cover of the plane by a lift of the cremona involution we also show that this gives a straightforward proof of the known description of the automorphism group for the generic such surface

a hybrid immune multiobjective optimization algorithm in this paper we develop a hybrid immune multiobjective optimization algorithm himo based on clonal selection principle in himo a hybrid mutation operator is proposed with the combination of gaussian and polynomial mutations gp hm operator the gp hm operator adopts an adaptive switching parameter to control the mutation process which uses relative large steps in high probability for boundary individuals and less crowded individuals with the generation running the probability to perform relative large steps is reduced gradually by this means the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search so as to accelerate the convergence speed to the true pareto optimal front in the global space with many local pareto optimal fronts when comparing himo with various state of the art multiobjective optimization algorithms developed recently simulation results show that himo performs better evidently

c psa constrained pareto simulated annealing for constrained multi objective optimization in recent years evolutionary algorithms eas have been extensively developed and utilized to solve multi objective optimization problems however some previous studies have shown that for certain problems an approach which allows for non greedy or uphill moves unlike eas can be more beneficial one such approach is simulated annealing sa sa is a proven heuristic for solving numerical optimization problems but owing to its point to point nature of search limited efforts has been made to explore its potential for solving multi objective problems the focus of the presented work is to develop a simulated annealing algorithm for constrained multi objective problems the performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems a comparison with other established multi objective optimization algorithms such as infeasibility driven evolutionary algorithm idea non dominated sorting genetic algorithm ii nsga ii and multi objective scatter search ii moss ii has been included to highlight the benefits of the proposed approach

an evolutionary artificial immune system for multi objective optimization in this paper an evolutionary artificial immune system for multi objective optimization which combines the global search ability of evolutionary algorithms and immune learning of artificial immune systems is proposed a new selection strategy is developed based upon the concept of clonal selection principle to maintain the balance between exploration and exploitation in order to maintain a diverse repertoire of antibodies an information theoretic based density preservation mechanism is also presented in addition the performances of various multi objective evolutionary algorithms as well as the effectiveness of the proposed features are examined based upon seven benchmark problems characterized by different difficulties in local optimality non uniformity discontinuity non convexity high dimensionality and constraints the comparative study shows the effectiveness of the proposed algorithm which produces solution sets that are highly competitive in terms of convergence diversity and distribution investigations also demonstrate the contribution and robustness of the proposed features

security based bi objective flow shop scheduling model and its hybrid genetic algorithm in this paper we considered the flow shop scheduling problem with respect to the both objectives of the makespan and the mean continuous running time proposed a security based bi objective flow shop scheduling model to solve the proposed model more effectively we presented a hybrid genetic algorithm hga which used some tailor made genetic operators and a local search operator in order to improve the local search ability of ga the proposed algorithm is tested with some well known problems in literature the computational results demonstrated the effectiveness of the proposed algorithm

an empirical investigation of meta heuristic and heuristic algorithms for a 2d packing problem abstract in this paper we consider the two dimensional 2d rectangular packing problem where a fixed set of items have to be allocated on a single object two heuristics which belong to the class of packing procedures that preserve bottom left bl stability are hybridised with three meta heuristic algorithms genetic algorithms ga simulated annealing sa naive evolution ne and local search heuristic hill climbing this study compares the hybrid algorithms in terms of solution quality and computation time on a number of packing problems of different size in order to show the effectiveness of the design of the different algorithms their performance is compared to random search rs and heuristic packing routines

a competitive and cooperative co evolutionary approach to multi objective particle swarm optimization algorithm design multi objective particle swarm optimization mopso is an optimization technique inspired by bird flocking which has been steadily gaining attention from the research community because of its high convergence speed on the other hand in the face of increasing complexity and dimensionality of today s application coupled with its tendency of premature convergence due to the high convergence speeds there is a need to improve the efficiency and effectiveness of mopso in this paper a competitive and cooperative co evolutionary approach is adapted for multi objective particle swarm optimization algorithm design which appears to have considerable potential for solving complex optimization problems by explicitly modeling the co evolution of competing and cooperating species the competitive and cooperative co evolution model helps to produce the reasonable problem decompositions by exploiting any correlation interdependency between components of the problem the proposed competitive and cooperative co evolutionary multi objective particle swarm optimization algorithm ccpso is validated through comparisons with existing state of the art multi objective algorithms using established benchmarks and metrics simulation results demonstrated that ccpso shows competitive if not better performance as compared to the other algorithms

convergence of multi objective evolutionary algorithms to a uniformly distributed representation of the pareto front in evolutionary multi objective optimization emo the convergence to the pareto set of a multi objective optimization problem mop and the diversity of the final approximation of the pareto front are two important issues in the existing definitions and analyses of convergence in multi objective evolutionary algorithms moeas convergence with probability is easily obtained because diversity is not considered however diversity cannot be guaranteed by combining the convergence with diversity this paper presents a new definition for the finite representation of a pareto set the b pareto set and a convergence metric for moeas based on a new archive updating strategy the convergence of one such moea to the b pareto sets of mops is proved numerical results show that the obtained b pareto front is uniformly distributed along the pareto front when according to the new definition of convergence the algorithm is convergent

multi objective robust pid controller tuning using two lbests multi objective particle swarm optimization in this paper two lbests multi objective particle swarm optimization 2lb mopso is applied to design multi objective robust proportional integral derivative pid controllers for two mimo systems namely distillation column plant and longitudinal control system of the super maneuverable f18 harv fighter aircraft multi objective robust pid controller design problem is formulated by minimizing integral squared error ise and balanced robust performance criteria during the search 2lb mopso can focus on small regions in the parameter space in the vicinity of the best existing fronts as the lbests are chosen from the top fronts in a non domination sorted external archive of reasonably large size the offspring obtained can be more diverse with good fitness the performance of various optimal pid controllers is compared in terms of the sum of ise and balanced robust performance criteria for the purpose of comparison 2lb mopso nsga ii as well as earlier reported riccati iga and osa methods are considered the performance of pid controllers obtained using 2lb mopso is better than that of others in addition hypervolume based comparisons are carried out to show the superior performance of 2lb mopso over nsga ii the results reveal that 2lb mopso yields better robustness and consistency in terms of the sum of ise and balanced robust performance criteria than various optimal pid controllers

genetic local search for multi objective combinatorial optimization abstract the paper presents a new genetic local search gls algorithm for multi objective combinatorial optimization moco the goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution in each iteration the algorithm draws at random a utility function and constructs a temporary population composed of a number of best solutions among the prior generated solutions then a pair of solutions selected at random from the temporary population is recombined local search procedure is applied to each offspring results of the presented experiment indicate that the algorithm outperforms other multi objective methods based on gls and a pareto ranking based multi objective genetic algorithm ga on travelling salesperson problem tsp

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

automorphisms of enriques surfaces o introduction the aim of this note is to compute the group aut y of biholomorphic auto morphisms for the general enriques surface y the basic tool is the global to relli theorem for projective k3 surfaces as it was given by piatetski shapiro and shafarevich 11 and refined by burns and rapaport 2 the essential result is that in contrast to the case of curves aut y is big for general y and small for special y in this paper we consider the complex case only recall that an enriques surface y is a projective complex surface with universal double cover a k3 surface one knows that h2 y z 7z 2 2 and that the cup product provides

a priori optimization consider a complete graph g v e in which each node is present with probability p we are interested in solving combinatorial optimization problems on subsets of nodes which are present with a certain probability we introduce the idea of a priori optimization as a strategy competitive to the strategy of reoptimization under which the combinatorial optimization problem is solved optimally for every instance we consider four problems the traveling salesman problem tsp the minimum spanning tree vehicle routing and traveling salesman facility location we discuss the applicability of a priori optimization strategies in several areas and show that if the nodes are randomly distributed in the plane the a priori and reoptimization strategies are very close in terms of performance we characterize the complexity of a priori optimization and address the question of approximating the optimal a priori solutions with polynomial time heuristics with provable worst case guarantees finally we use the tsp as an example to find practical solutions based on ideas of local optimality

resource allocation model and double sphere crowding distance for evolutionary multi objective optimization abstract convergence speed and diversity of nondominated solutions are two important performance indicators for multi objective evolutionary algorithms moeas in this paper we propose a resource allocation ra model based on game theory to accelerate the convergence speed of moeas and a novel double sphere crowding distance dscd measure to improve the diversity of nondominated solutions the mechanism of ra model is that the individuals in each group cooperate with each other to get maximum benefits for their group and then individuals in the same group compete for private interests the dscd measure uses hyper spheres consisting of nearest neighbors to estimate the crowding degree experimental results on convergence speed and diversity of nondominated solutions for benchmark problems and a real world problem show the efficiency of these two proposed techniques

an optimal image watermarking approach based on a multi objective genetic algorithm in accordance with the multi objective nature of image watermarking an optimal image watermarking approach using a multi objective genetic algorithm is presented in this paper both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems the proposed multi objective watermarking method can automatically optimize system parameters and a variable length mechanism is specially designed to search the most suitable positions for embedding watermarks the method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms the proposed multi objective watermarking method directly deals with the problem of optimizing watermarking under non dominated meaning thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single objective watermarking schemes in addition a pareto optimal set generated by multi objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements

on convergence of the multi objective particle swarm optimizers several variants of the particle swarm optimization pso algorithm have been proposed in recent past to tackle the multi objective optimization mo problems based on the concept of pareto optimality although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of pso as a single objective optimizer till date to the best of our knowledge no such analysis exists for the multi objective pso mopso algorithms this paper presents a first simple analysis of the general pareto based mopso and finds conditions on its most important control parameters the inertia factor and acceleration coefficients that govern the convergence behavior of the algorithm to the optimal pareto front in the objective function space computer simulations over benchmark mo problems have also been provided to substantiate the theoretical derivations

solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation abstract this paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer the solution to this vehicle routing problem with stochastic demand vrpsd involves the optimization of complete routing schedules with minimum travel distance driver remuneration and number of vehicles subject to a number of constraints such as time windows and vehicle capacity to solve such a multiobjective and multi modal combinatorial optimization problem this paper presents a multiobjective evolutionary algorithm that incorporates two vrpsd specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions a new way of assessing the quality of solutions to the vrpsd on top of comparing their expected costs is also proposed it is shown that the algorithm is capable of finding useful tradeoff solutions for the vrpsd and the solutions are robust to the stochastic nature of the problem the developed algorithm is further validated on a few vrpsd instances adapted from solomon s vehicle routing problem with time windows vrptw benchmark problems

modeling vehicle routing with uncertain demands as a stochastic program properties of the corresponding solution abstract in this paper we address the issue of different mathematical models for the stochastic vehicle routing problem svrp this problem is inherently much more difficult than the generic deterministic vehicle routing problem vrp for which optimal procedures can solve only small problems presently we cannot even begin optimal solution procedures for the svrp for any problem size exceeding 3 nodes thus we need to examine modeling approaches to this problem in order to exploit the structure and solution properties we present a multistate stochastic model for the svrp we prove that this model has an interesting minimal graph representation in which a svrp solution corresponds to a hamiltonian cycle we also present a markov decision model for the problem concluding with a discussion of solution prospects and directions

on the estimation of residual stresses by the crack compliance method the crack compliance method is a destructive experimental method used for the estimation of residual stress profiles in thick metal plates simplifying assumptions such as dimensional reduction are generally used in applications of this method the question of how the simplifying assumptions affect the estimated residual stresses is addressed in this paper

a more efficient attribute self adaptive co evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators abstract in order to further improve the adaptability of attribute reduction and enhance its application performance in large scale attribute reduction a more efficient attribute self adaptive co evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators qecmascr is proposed in this paper firstly quantum chromosome is used to encode the evolutionary population and a multilevel elitist pool of quantum frogs is constructed in which quantum elitist frogs can fast guide the evolutionary population into the optimal area secondly a reversible cloud mode based on attribute entropy weight is designed to adjust the quantum cloud revolving angle so that the scope of search space can be adaptively controlled under the guidance of qualitative knowledge in addition both the quantum cloud mutation operator and quantum cloud entanglement operator are used to make quantum frogs be adaptive to get the optimal set of attribute reduction fast thirdly an improved decomposition framework of attribute self adaptive co evolution is adopted to capture interdependencies of decision variables it can decompose the large scale attribute set into reasonable scale subsets according to two kinds of the best performance fitness and assignment credit thus some optimal elitists in different memeplexes of multilevel elitist pool are selected out to evolve their representing attribute subsets which can increase the cooperation and efficiency of attribute reduction so the global minimum attribute reduction can be achieved steadily and efficiently experimental results indicate the proposed qecmascr algorithm achieves the better superior performance than existing representative algorithms moreover it is applied into mri segmentation and the effective and robust segmentation results further demonstrate its stronger applicability

fuzzy multiobjective modeling and optimization for one shot multiattribute exchanges with indivisible demand the modern economy involves a variety of marketplaces and the internet has led to the development of a new efficient marketplace the one shot multiattribute exchange it is an important decision problem for a matchmaker or broker to achieve the optimal trade matching in one shot multiattribute exchanges however to the best of our knowledge there has been little work on this issue under fuzzy environments this paper proposes an optimal matching approach for one shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations first we employ fuzzy set theory to represent the traders orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom second on the basis of the matching degree we construct a fuzzy multiobjective programming model for one shot multiattribute exchanges with indivisible demand afterward the credibility measure is introduced to convert the model into a crisp one the crisp model belongs to a class of multiobjective nonlinear general assignment problems and has np hard complexity in order to solve the crisp model effectively we develop a problem specified metaheuristic algorithm i e multiobjective discrete differential evolution finally we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm

a multiobjective immune algorithm based on a multiple affinity model abstract this paper presents a new multiobjective immune algorithm based on a multiple affinity model inspired by immune system mam moia the multiple affinity model builds the relationship model among main entities and concepts in multiobjective problems mops and multiobjective evolutionary algorithms moeas including feasible solution variable space objective space pareto optimal set ranking and crowding distance in the model immune operators including clonal proliferation hypermutation and immune suppression are designed to proliferate superior antibodies and suppress the inferiors mam moia is compared with nsga ii spea2 and nnia in solving the zdt and dtlz standard test problems the experimental study based on three performance metrics including coverage of two sets convergence and spacing proves that mam moia is effective for solving mops

on solving multiobjective bin packing problems using evolutionary particle swarm optimization abstract the bin packing problem is widely found in applications such as loading of tractor trailer trucks cargo airplanes and ships where a balanced load provides better fuel efficiency and safer ride in these applications there are often conflicting criteria to be satisfied i e to minimize the bins used and to balance the load of each bin subject to a number of practical constraints unlike existing studies that only consider the issue of minimum bins a multiobjective two dimensional mathematical model for bin packing problems with multiple constraints mobpp 2d is formulated in this paper to solve mobpp 2d problems a multiobjective evolutionary particle swarm optimization algorithm moepso is proposed without the need of combining both objectives into a composite scalar weighting function moepso incorporates the concept of pareto s optimality to evolve a family of solutions along the trade off surface extensive numerical investigations are performed on various test instances and their performances are compared both quantitatively and statistically with other optimization methods to illustrate the effectiveness and efficiency of moepso in solving multiobjective bin packing problems

worst case analysis of heuristics for the bin packing problem with general cost structures we consider the famous bin packing problem where a set of items must be stored in bins of equal capacity in the classical version the objective is to minimize the number of bins used motivated by several optimization problems that occur in the context of the storage of items we study a more general cost structure where the cost of a bin is a concave function of the number of items in the bin the objective is to store the items in such a way that total cost is minimized such cost functions can greatly alter the way the items should be assigned to the bins we show that some of the best heuristics developed for the classical bin packing problem can perform poorly under the general cost structure on the other hand the so called next fit increasing heuristic has an absolute worst case bound of no more than 1 75 and an asymptotic worst case bound of 1 691 for any concave and monotone cost function our analysis also provides a new worst case bound for the well studied next tit decreasing heuristic when

a hybrid genetic local search algorithm for the permutation flowshop scheduling problem traditionally the permutation flowshop scheduling problem pfsp was with the criterion of minimizing makespan the permutation flowshop scheduling problem to minimize the total flowtime has attracted more attention from researchers in recent years in this paper a hybrid genetic local search algorithm is proposed to solve this problem with each of both criteria the proposed algorithm hybridizes the genetic algorithm and a novel local search scheme that combines two local search methods the insertion search is and the insertion search with cut and repair iscr it employs the genetic algorithm to do the global search and two local search methods to do the local search two local search methods play different roles in the search process the insertion search is responsible for searching a small neighborhood while the insertion search with cut and repair is responsible for searching a large neighborhood furthermore the orthogonal array based crossover operator is designed to enhance the ga s capability of intensification the experimental results show the advantage of combining the two local search methods the performance of the proposed hybrid genetic algorithm is very competitive for the pfsp with the total flowtime criterion it improved 66 out of the 90 current best solutions reported in the literature in short term search and it also improved all the 20 current best solutions reported in the literature in long term search for the pfsp with the makespan criterion the proposed algorithm also outperforms the other three methods recently reported in the literature

on the generalized plane strain problem in thermoelasticity the reliability of the solutions of problems of two dimensional linear elasticity is examined in relation to the solutions of the corresponding three dimensional problems it is shown that the solution of a long cylindrical body with free ends can be approximated by the classical plane strain solution plus a correction which is the solution of another two dimensional problem the corrected solution is exponentially accurate sufficiently far from the ends in the vicinity of the free ends a three dimensional model has to be used

a dominance tree and its application in evolutionary multi objective optimization most contemporary multi objective evolutionary algorithms moeas store and handle a population with a linear list and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes this paper presents a data structure for storing the whole population and their dominating information in moeas this structure called a dominance tree dt is a binary tree that can effectively and efficiently store three valued relations namely dominating dominated or non dominated among vector values this paper further demonstrates dt s potential applications in evolutionary multi objective optimization with two cases the first case utilizes the dt to improve nsga ii as a fitness assignment strategy the second case demonstrates a dt based moea called a dtea which is designed by leveraging the favorable properties of the dt the simulation results show that the dt improved nsga ii is significantly faster than nsga ii meanwhile dtea is much faster than spea2 nsga ii and an improved version of nsga ii on the other hand in regard to converging to the pareto optimal front and maintaining the diversity of solutions dt improved nsga ii and dtea are found to be competitive with nsga ii and spea2

a binary particle swarm optimization algorithm inspired by multi level organizational learning behavior abstract recently nature inspired algorithms have increasingly attracted the attention of researchers due to the fact that in bpso the position vectors consisting of 0 and 1 can be seen as a decision behavior support or oppose in this paper we propose a bpso with hierarchical structure bpso hs for short on the basis of multi level organizational learning behavior at each iteration of bpso hs particles are divided into two classes named leaders and followers and different evolutionary strategies are used in each class in addition the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization the algorithm was tested on two discrete optimization problems traveling salesman and bin packing as well as seven real parameter functions the experimental results showed that the performance of bpso hs was significantly better than several existing algorithms

achieving balance between proximity and diversity in multi objective evolutionary algorithm currently an alternative framework using the hypervolume indicator to guide the search for elite solutions of a multi objective problem is studied in the evolutionary multi objective optimization community very actively comparing to the traditional pareto dominance based approach in this paper we present a dynamic neighborhood multi objective evolutionary algorithm based on hypervolume indicator dnmoea hi which benefits from both pareto dominance and hypervolume indicator based frameworks dnmoea hi is featured by the employment of hypervolume indicator as a truncation operator to prune the exceeded population while a well designed density estimator i e tree neighborhood density is combined with the pareto strength value to perform fitness assignment moreover a novel algorithm is proposed to directly evaluate the hypervolume contribution of a single individual the performance of dnmoea hi is verified on a comprehensive benchmark suite in comparison with six other multi objective evolutionary algorithms experimental results demonstrate the efficiency of our proposed algorithm solutions obtained by dnmoea hi well approach the pareto optimal front and are evenly distributed over the front simultaneously

an empirical study on similarity based mating for evolutionary multiobjective combinatorial optimization we have already proposed a similarity based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization in this paper we examine the effect of the similarity based mating scheme on the performance of evolutionary multiobjective optimization emo algorithms first we examine which is better between recombining similar or dissimilar parents next we examine the effect of biasing selection probabilities toward extreme solutions that are dissimilar from other solutions in each population then we examine the effect of dynamically changing the strength of this bias during the execution of emo algorithms computational experiments are performed on a wide variety of test problems for multiobjective combinatorial optimization experimental results show that the performance of emo algorithms can be improved by the similarity based mating scheme for many test problems

the three dimensional bin packing problem the problem addressed in this paper is that of orthogonally packing a given set of rectangular shaped items into the minimum number of three dimensional rectangular bins the problem is strongly np hard and extremely difficult to solve in practice lower bounds are discussed and it is proved that the asymptotic worst case performance ratio of the continuous lower bound is an exact algorithm for filling a single bin is developed leading to the definition of an exact branch and bound algorithm for the three dimensional bin packing problem which also incorporates original approximation algorithms extensive computational results involving instances with up to 90 items are presented it is shown that many instances can be solved to optimality within a reasonable time limit

enhanced speciation in particle swarm optimization for multi modal problems abstract in this paper we present a novel multi modal optimization algorithm for finding multiple local optima in objective function surfaces we build from species based particle swarm optimization spso by using deterministic sampling to generate new particles during the optimization process by implementing proximity based speciation coupled with speciation of isolated particles and by including turbulence regions around already found solutions to prevent unnecessary function evaluations instead of using error threshold values the new algorithm uses the particle s experience geometric mean and exclusion factor to detect local optima and stop the algorithm the performance of each extension is assessed with leave it out tests and the results are discussed we use the new algorithm called isolated speciation based particle swarm optimization ispso and a benchmark algorithm called niche particle swarm optimization nichepso to solve a six dimensional rainfall characterization problem for 192 rain gages across the united states we show why it is important to find multiple local optima for solving this real world complex problem by discussing its high multi modality solutions found by both algorithms are compared and we conclude that ispso is more reliable than nichepso at finding optima with a significantly lower objective function value

eidetic wolf search algorithm with a global memory structure abstract a recently proposed metaheuristics called wolf search algorithm wsa has demonstrated its efficacy for various hard to solve optimization problems in this paper an improved version of wsa namely eidetic wsa with a global memory structure gms or just ewsa is presented ewsa makes use of gms for improving its search for the optimal fitness value by preventing mediocre visited places in the search space to be visited again in future iterations inherited from swarm intelligence search agents in ewsa and the traditional wsa merge into an optimal solution although the agents behave and make decisions autonomously heuristic information gathered from collective memory of the swarm search agents is stored in gms the heuristics eventually leads to faster convergence and improved optimal fitness the concept is similar to a hybrid metaheuristics based on wsa and tabu search ewsa is tested with seven standard optimization functions rigorously in particular ewsa is compared with two state of the art metaheuristics ant colony optimization aco and particle swarm optimization pso ewsa shares some similarity with both approaches with respect to directed random search the similarity with aco is however stronger as aco uses pheromones as global information references that allow a balance between using previous knowledge and exploring new solutions under comparable experimental settings identical population size and number of generations ewsa is shown to outperform both aco and pso with statistical significance when dedicating the same computation time only aco can be outperformed due to a comparably long run time per iteration of ewsa

algorithms for the vehicle routing and scheduling problems with time window constraints this paper considers the design and analysis of algorithms for vehicle routing and scheduling problems with time window constraints given the intrinsic difficulty of this problem class approximation methods seem to offer the most promise for practical size problems after describing a variety of heuristics we conduct an extensive computational study of their performance the problem set includes routing and scheduling environments that differ in terms of the type of data used to generate the problems the percentage of customers with time windows their tightness and positioning and the scheduling horizon we found that several heuristics performed well in different problem environments in particular an insertion type heuristic consistently gave very good results

a multi objective memetic algorithm based on locality sensitive hashing for one to many to one dynamic pickup and delivery problem this paper presents an early attempt to solve one to many to one dynamic pickup and delivery problem dpdp by proposing a multi objective memetic algorithm called lsh moma which is a synergy of multi objective evolutionary algorithm and locality sensitive hashing lsh based local search three objectives namely route length response time and workload are optimized simultaneously in an evolutionary framework in each generation of lsh moma lsh based rectification and local search are imposed to repair and improve the individual solutions lsh moma is evaluated on four benchmark dpdps and the experimental results show that lsh moma is efficient in obtaining optimal tradeoff solutions of the three objectives

multi objective evolutionary algorithms based on the summation of normalized objectives and diversified selection most multi objective evolutionary algorithms moeas use the concept of dominance in the search process to select the top solutions as parents in an elitist manner however as moeas are probabilistic search methods some useful information may be wasted if the dominated solutions are completely disregarded in addition the diversity may be lost during the early stages of the search process leading to a locally optimal or partial pareto front beside this the non domination sorting process is complex and time consuming to overcome these problems this paper proposes multi objective evolutionary algorithms based on summation of normalized objective values and diversified selection snov ds the performance of this algorithm is tested on a set of benchmark problems using both multi objective evolutionary programming moep and multi objective differential evolution mode with the proposed method the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non domination sorting in addition the proposed algorithm also outperforms ten other algorithms

large scale evolutionary optimization using cooperative coevolution evolutionary algorithms eas have been applied with success to many numerical and combinatorial optimization problems in recent years however they often lose their effectiveness and advantages when applied to large and complex problems e g those with high dimensions although cooperative coevolution has been proposed as a promising framework for tackling high dimensional optimization problems only limited studies were reported by decomposing a high dimensional problem into single variables dimensions such methods of decomposition often failed to solve nonseparable problems for which tight interactions exist among different decision variables in this paper we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems a random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution instead of conventional evolutionary algorithms a novel differential evolution algorithm is adopted theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions the results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems we are unaware of any other evolutionary algorithms that can optimize 1000 dimension nonseparable problems as effectively and efficiently as we have done

a tabu search heuristic for the vehicle routing problem with stochastic demands and customers this paper considers a version of the stochastic vehicle routing problem where customers are present at locations with some probabilities and have random demands a tabu search heuristic is developed for this problem comparisons with known optimal solutions on problems whose sizes vary from 6 to 46 customers indicate that the heuristic produces an optimal solution in 89 45 of cases with an average deviation of 0 38 from optimality

a particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem abstract in this paper a particle swarm optimization algorithm pso is presented to solve the permutation flowshop sequencing problem pfsp with the objectives of minimizing makespan and the total flowtime of jobs for this purpose a heuristic rule called the smallest position value spv borrowed from the random key representation of bean j c bean genetic algorithm and random keys for sequencing and optimization orsa journal of computing 6 2 1994 154 160 was developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems in addition a very efficient local search called variable neighborhood search vns was embedded in the pso algorithm to solve the well known benchmark suites in the literature the pso algorithm was applied to both the 90 benchmark instances provided by taillard e taillard benchmarks for basic scheduling problems european journal of operational research 64 1993 278 285 and the 14 000 random narrow random and structured benchmark instances provided by watson et al j p watson l barbulescu l d whitley a e howe contrasting structured and random permutation flowshop scheduling problems search space topology and algorithm performance orsa journal of computing 14 2 2002 98 123 for makespan criterion the solution quality was evaluated according to the best known solutions provided either by taillard or watson et al the total flowtime criterion was evaluated with the best known solutions provided by liu and reeves j liu c r reeves constructive and composite heuristic solutions to the p ci scheduling problem european journal of operational research 132 2001 439 452 and rajendran and ziegler c rajendran h ziegler ant colony algorithms for permutation flowshop scheduling to minimize makespan total flowtime of jobs european journal of operational research 155 2 2004 426 438 for the total flowtime criterion 57 out of the 90 best known solutions reported by liu and reeves and rajendran and ziegler were improved whereas for the makespan criterion 195 out of the 800 best known solutions for the random and narrow random problems reported by watson et al were improved by the vns version of the pso algorithm

a discrete particle swarm optimization for lot streaming flowshop scheduling problem we consider an n job m machine lot streaming problem in a flowshop with equal size sublots where the objective is to minimize the total weighted earliness and tardiness to solve this problem we first propose a so called net benefit of movement nbm algorithm which is much more efficient than the existing linear programming model for obtaining the optimal starting and completion times of sublots for a given job sequence a new discrete particle swarm optimization dpso algorithm incorporating the nbm algorithm is then developed to search for the best sequence the new dpso improves the existing dpso by introducing an inheritance scheme inspired by a genetic algorithm into particles construction to verify the proposed dpso algorithm comparisons with the existing dpso algorithm and a hybrid genetic algorithm hga are made computational results show that the proposed dpso algorithm with a two point inheritance scheme is very competitive for the lot streaming flowshop scheduling problem

artificial immune multi objective sar image segmentation with fused complementary features artificial immune systems ais are the computational systems inspired by the principles and processes of the vertebrate immune system ais based algorithms typically mimic the human immune system s characteristics of learning and adaptability to solve some complicated problems here an artificial immune multi objective optimization framework is formulated and applied to synthetic aperture radar sar image segmentation the important innovations of the framework are listed as follows 1 an efficient and robust immune multi objective optimization algorithm is proposed which has the features of adaptive rank clones and diversity maintenance by k nearest neighbor list 2 besides two conflicting fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and 3 moreover an effective fused feature set for texture representation and discrimination is constructed and researched which utilizes both the gabor filter s ability to precisely extract texture features in low and mid frequency components and the gray level co occurrence probability s glcp ability to measure information in high frequency two experiments with synthetic texture images and sar images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms fuzzy c means fcm single objective genetic algorithm soga self organizing map som wavelet domain hidden markov models hmtseg and spectral clustering ensemble sce experimental results show the proposed framework has obtained the better performance in segmenting sar images than other five algorithms and behaves insensitive to the speckle noise

nikulin involutions on k3 surfaces we study the maps induced on cohomology by a nikulin i e a symplectic involution on a k3 surface we parametrize the 11 dimensional irreducible components of the moduli space of algebraic k3 surfaces with a nikulin involution and we give examples of the general k3 surface in various components we conclude with some remarks on morrison nikulin involutions these are nikulin involutions which interchange two copies of e 8 1 in the neron severi group

general framework for localised multi objective evolutionary algorithms many real world problems have multiple competing objectives and can often be formulated as multi objective optimisation problems multi objective evolutionary algorithms moeas have proven very effective in obtaining a set of trade off solutions for such problems this research seeks to improve both the accuracy and the diversity of these solutions through the local application of evolutionary operators to selected sub populations a local operation based implementation framework is presented in which a population is partitioned using hierarchical clustering into a pre defined number of sub populations environment selection and genetic variation are then applied to each sub population the effectiveness of this approach is demonstrated on 2 and 4 objective benchmark problems the performance of each of four best in class moeas is compared with their modified local operation based versions derived from this framework in each case the introduction of the local operation based approach improves performance further it is shown that the combined use of local environment selection and local genetic variation is better than the application of either local environment selection or local genetic variation alone preliminary results indicate that the selection of a suitable number of sub populations is related to problem dimension as well as to population size