Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization
David Ireland, Andrew W. Lewis, Sanaz Mostaghim, Junwei Lu · 2006
This paper presents quantitative comparison of the performance of different methods for selecting the guide particle for multi-objective particle swarm optimization (MOPSO). Two principal methods are compared: the recently described Sigma method, and a new “Centroid” method. Drawing on the different dominant behaviors exhibited by the different selection methods, a variety of hybridizations of these is proposed to develop a more robust optimization algorithm. Statistical analysis of the hybrid methods demonstrates their contribution to improved performance of the optimization algorithm.