A New Particle Swarm Optimization Based on Grey Self-Linkage Analysis

A new particle swarm optimization based on grey self-linkage analysis was presented. In order to overcome premature of standard particle swarm optimization, the proposed algorithm considered the interrelations between problem dimensions, which perform more frequent simultaneous updates on subsets of particle position components that are strongly linked and using new speed-location update formula. Compared with other improved PSO algorithms, the testing results indicate that the new algorithm has better probability of convergence rate and accuracy for single-mode functions. The further work is how to improve the global searching performance effectively to multi-modal problems.