Distance-based clustering of population and intergroup Cooperative Particle Swarm Optimization

Sun and Li (2014) have proposed TCPSO(Two-swarm Cooperative Particle Swarm Optimization) that the swarms are divided into two groups with different migration rules. TCPSO has higher performance for high-dimensional nonlinear optimization problems. This study revises TCPSO to avoid inappropriate convergence of the swarms. The quite feature of the proposed method is that the population have same migration rules. However, through that the swarms are divided into some clusters based on distance measure, k-means clustering method, both diversity and centralization of search process are maintained, and it increases the potential of attainment to the global optimal solution. This study conducts numerical experiments using several types of functions, and the experimental results indicate that the proposed method has higher performance than the TCPSO for large-scale optimization problems.