Discrete Multi-Phase Particle Swarm Optimization

This chapter proposes the Discrete Multi-Phase Particle Swarm Optimization (DiMuPSO) algorithm, extending the PSO approach to problems coded with discrete binary representations. The main features of DiMuPSO are in utilizing multiple groups of particles with different goals that are allowed to change with time, alternately moving toward or away from the best solutions found recently. DiMuPSO also enforces steady improvement in solution quality, accepting only moves that improve fitness. Experimental simulations show that DiMuPSO outperforms a genetic algorithm and a previous discrete version of PSO on several benchmark problems.

[1]  Adam Prügel-Bennett,et al.  Genetic drift in genetic algorithm selection schemes , 1999, IEEE Trans. Evol. Comput..

[2]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[3]  Chilukuri K. Mohan,et al.  Multi-phase Discrete Particle Swarm Optimization , 2002, JCIS.

[4]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale , 1990, Complex Syst..

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Cyril Fonlupt,et al.  A Shepherd and a Sheepdog to Guide Evolutionary Computation? , 1999, Artificial Evolution.

[7]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[8]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[9]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[10]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[11]  Kalyanmoy Deb,et al.  Massive Multimodality, Deception, and Genetic Algorithms , 1992, PPSN.

[12]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[13]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

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

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Mark M. Millonas,et al.  Swarms, Phase Transitions, and Collective Intelligence , 1993, adap-org/9306002.

[17]  R. B. Patil.  Intervals in evolutionary algorithms for global optimization , 1995 .

[18]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

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

[21]  C. Mohan,et al.  Multi-phase generalization of the particle swarm optimization algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[22]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[23]  Deniz Yuret.  From Genetic Algorithms to Efficient Optimization , 1994 .