Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization

Vector evaluated particle swarm optimization (VEPSO) is a multi-swarm variant of the traditional particle swarm optimization (PSO) algorithm applied to multi-objective problems (MOPs). Each sub-objective is allocated a single sub-swarm and knowledge transfer strategies (KTSs) are used to pass information between swarms. The original VEPSO used a ring KTS, and while VEPSO has shown to be successful in solving MOPs, other algorithms have been shown to produce better results. One reason for VEPSO to perform worse than other algorithms may be due to the inefficiency of the KTS used in the original VEPSO. This paper investigates new KTSs for VEPSO in order to improve its performance. The results indicated that a hybrid strategy using parent-centric crossover (PCX) on global best solutions generally lead to a higher hypervolume while using PCX on archive solutions generally lead to a better distributed set of solutions.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[3]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[4]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[5]  John J. Grefenstette,et al.  Proceedings of the 1st International Conference on Genetic Algorithms , 1985 .

[6]  Salvatore Greco,et al.  Evolutionary Multi-Criterion Optimization , 2011, Lecture Notes in Computer Science.

[7]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[8]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[9]  Nadia Nedjah,et al.  Multi-Objective Swarm Intelligent Systems - Theory & Experiences , 2010, Multi-Objective Swarm Intelligent Systems.

[10]  Andries Petrus Engelbrecht,et al.  Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[11]  Nikhil Padhye.  Comparison of archiving methods in multi-objectiveparticle swarm optimization (MOPSO): empirical study , 2009, GECCO '09.

[12]  Kay Chen Tan,et al.  An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[13]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[14]  Kwang Y. Lee,et al.  Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems , 2009, Expert Syst. Appl..

[15]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[16]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[17]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part I , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[18]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[19]  Jacomine Grobler,et al.  Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling , 2009 .

[20]  Kim Fung Man,et al.  Multiobjective Optimization , 2011, IEEE Microwave Magazine.

[21]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[22]  Theodor J. Stewart,et al.  Real-World Applications of Multiobjective Optimization , 2008, Multiobjective Optimization.

[23]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[24]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Andries Petrus Engelbrecht,et al.  Dynamic Multi-objective Optimisation Using PSO , 2010, Multi-Objective Swarm Intelligent System.