Particle swarm guided evolution strategy

Evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics for tackling real-parameter optimization problems in evolutionary computation. Both of them have strengths and weaknesses for their different search behaviors and methodologies. In ES, mutation, as the main operator, tries to find good solutions around each individual. While in PSO, particles are moving toward directions determined by certain global information, such as the global best particle. In order to leverage the specialties offered by both sides to our advantage, this paper combines the essential mechanism of ES and the key concept of PSO to develop a new hybrid optimization methodology, called particle swarm guided evolution strategy. We introduce swarm intelligence to the ES mutation framework to create a new mutation operator, called guided mutation, and integrate the guided mutation operator into ES. Numerical experiments are conducted on a set of benchmark functions, and the experimental results indicate that PSGES is a promising optimization methodology as well as an interesting research direction.

[1]  Marcus Gallagher,et al.  Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA , 2005, 2005 IEEE Congress on Evolutionary Computation.

[2]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[3]  Francisco Herrera,et al.  Adaptive local search parameters for real-coded memetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[4]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[5]  Vladimiro Miranda,et al.  NEW EVOLUTIONARY PARTICLE SWARM ALGORITHM (EPSO) APPLIED TO VOLTAGE/VAR CONTROL , 2002 .

[6]  Lino A. Costa,et al.  A parameter-less evolution strategy for global optimization , 2006 .

[7]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[8]  Vladimiro Miranda,et al.  EPSO - best-of-two-worlds meta-heuristic applied to power system problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  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.

[10]  Petr Posík,et al.  Real-parameter optimization using the mutation step co-evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[11]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[13]  Anne Auger,et al.  Performance evaluation of an advanced local search evolutionary algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  G. Unter Rudolph.  On Correlated Mutations in Evolution Strategies , 1992 .

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

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

[17]  Kalyanmoy Deb,et al.  A population-based, steady-state procedure for real-parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[18]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[19]  Pedro J. Ballester,et al.  Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX , 2005, 2005 IEEE Congress on Evolutionary Computation.

[20]  Lars Hildebrand,et al.  Directed mutation-a new self-adaptation for evolution strategies , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[21]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .