Merging and Decomposition Variants of Cooperative Particle Swarm Optimization
J. D. Douglas, Andries Petrus Engelbrecht, Beatrice M. Ombuki-Berman · 2018
Many optimization algorithms suffer under the curse of dimensionality - a problem that describes a decrease in performance as the number of problem variables increases. Particle swarm optimization (PSO) is no exception to this performance degradation. Cooperative methods have since been introduced for PSO in order to increase its effectiveness for high dimensional problems by following a divide and conquer approach to large dimensional problems. Performance still suffers for such cooperative PSO (CPSO) variants, mostly due to the dependencies among variables. This paper demonstrates the performance of two new variants of CPSO, namely decomposition and merging cooperative swarm optimization, referred to as DCPSO and MCPSO respectively. The goal of these variants is to improve performance for large scale problems with variable dependencies. Empirical results show that DCPSO and MCPSO were able to perform better than standard CPSO-Sk, CPSOHk and a random grouping variant, CCPSO, for specific problem classes. While empirical results show that the best strategy between merging and decomposition is very much problem dependent, the MCPSO generally converged earlier than the DCPSO.