Cooperative Particle Swarm Optimization Decomposition Methods for Large-scale Optimization

Many practical optimization problems can be classified as large-scale global optimization problems (LSOP). Various cooperative co-evolutionary (CC) algorithms have been proposed to combat the challenges of LSOPs. When CC algorithms are applied to large scale optimization problems, the effects of interconnected variables, known as variable dependencies, can cause major performance degradation. Current literature provides different approaches to decomposing large-scale problems with variable dependencies during optimization using a wide range of base optimizers. In this paper, a cooperative particle swarm optimization (CPSO) algorithm is used as the base optimizer in a scalability study with a range of decomposition methods to determine ideal divide-and-conquer approaches when using a CPSO. Experimental results demonstrate that a variety of dynamic regrouping of variables, seen in the merging CPSO (MCPSO) and decomposition CPSO (DCPSO), as well varying total fitness evaluations per dimension, results in high-quality solutions when compared to six state-of-the-art decomposition approaches.