A comprehensive study of particle swarm based multi-objective optimization

Abstract

Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. This paper presents a comprehensive study of Multi-Objective Optimization (MOO) with Particle Swarm Optimization (PSO). Different suggestions of various researchers have been compiled to give a first-hand information of PSO based MOO. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user's preferences, the solution requirements and the availability of software. © 2012 Springer-Verlag GmbH Berlin Heidelberg.