CCPSO Based on PCA for Large-Scale Optimization Problem

Abstract

This paper proposed a cooperative coevolving particle swarm optimization base on principal component analysis (PCA-CCPSO) algorithm for large-scale and complex problem. In this algorithm, PCA are used to pick up the available particles which gathered the important information of the initialized particles for CCPSO. The Cauchy and Gaussian distributions are used to update the position of the particles and the coevolving subcomponent size of the particles is determined dynamically. The experimental results demonstrate that the convergence speed of PCA-CCPSO is faster than that of CCPSO in solving the large-scale and complex multimodal optimization problems.