Observing the swarm behaviour during its evolutionary design

Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of design PSO algorithm we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm's behaviours. The observed rules can help us to design better PSO algorithms for optimization. In this paper we investigate and analyze swarm phenomena by looking to process of evolving PSO algorithms. Several interesting facts are inferred from the strategy evolution process (the particle quality could influence the update order, some particles are updated more frequently than others are, the initial swarm size is not always optimal).

[1]  Riccardo Poli,et al.  Extending Particle Swarm Optimisation via Genetic Programming , 2005, EuroGP.

[2]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

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

[4]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Tim Hendtlass,et al.  A Combined Swarm Differential Evolution Algorithm for Optimization Problems , 2001, IEA/AIE.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[9]  Angel Eduardo Muñoz Zavala,et al.  Particle evolutionary swarm optimization algorithm (PESO) , 2005, Sixth Mexican International Conference on Computer Science (ENC'05).

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

[12]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[13]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[14]  Jigui Sun,et al.  An Improved Discrete Particle Swarm Optimization Algorithm for TSP , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[15]  Riccardo Poli,et al.  Kernel methods for PSOs , 2005 .

[16]  A. E. Eiben,et al.  Evolutionary Programming VII , 1998, Lecture Notes in Computer Science.

[17]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[19]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Christian Jacob,et al.  Evolutionary exploration of dynamic swarm behaviour , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[21]  Mihai Oltean,et al.  Evolving the Structure of the Particle Swarm Optimization Algorithms , 2006, EvoCOP.

[22]  Byung-Il Koh,et al.  Parallel asynchronous particle swarm optimization , 2006, International journal for numerical methods in engineering.

[23]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[24]  E. Ozcan,et al.  Particle swarm optimization: surfing the waves , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[25]  Russell C. Eberhart,et al.  Recent advances in particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[26]  James Kennedy,et al.  The Behavior of Particles , 1998, Evolutionary Programming.

[27]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).