Differential evolution in high-dimensional search spaces

A possible way of dealing with a high dimensional problem space is to divide it up into smaller parts, and to have each part optimized by a separate population. A mechanism is then defined to construct a complete solution from the subpopulations, and to evaluate the entities contained in the subpopulations. This form of cooperation has been successfully applied to particle swarm optimization (PSO), by [1] in the cooperative split PSO, and to genetic algorithms, in the cooperative coevolutionary genetic algorithm, developed by [2], on which the cooperative split PSO is based. This paper investigates cooperation in differential evolution (DE) with the aim of determining the effects of multiple participants in dealing with high-dimensional problem spaces.

[1]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  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.

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

[4]  Andries Petrus Engelbrecht,et al.  Cooperative learning in neural networks using particle swarm optimizers , 2000, South Afr. Comput. J..

[5]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[6]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[7]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[9]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[10]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[11]  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).