Empirical comparison of MOPSO methods - Guide selection and diversity preservation -

Abstract

In this paper, we review several proposals for guide selection in Multi-Objective Particle Swarm Optimization (MOPSO) and compare them with each other in terms of convergence, diversity and computational times. The new proposals made for guide selection, both personal best (dasiapbestpsila) and global best (dasiagbestpsila), are found to be extremely effective and perform well compared to the already existing methods. The combination of selection methods for choosing dasiagbestpsila and dasiapbestpsila is also studied and it turns out that there exist certain combinations which yield an overall superior performance outperforming the others on the tested benchmark problems. Furthermore, two new proposals namely velocity trigger (as a substitute for ldquoturbulence operatorrdquo) and a new scheme of boundary handling is made.