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The main difference between single and multi-objective optimizations using particle swarm optimization is how the guider to locate the global optimal and Pareto optimal solutions are defined in corresponding optimization problems. In general multi-objective particle swarm optimization, only one guider is selected, and, in order to reduce the non-dominated solution for more diversity in the external archive, only crowding distance in objective space is considered. This paper presents a new approach of selecting multiple guiders to lead a swarm toward a Pareto-front. Additionally, in order to overcome the local Pareto front, mutation operator is applied for not only particles but also members in an external archive. Furthermore, aside from considering the crowding distance of solutions in objective space to maintain the diversity of solutions, the crowding distance in variable space is also taken into account. The proposed algorithm is compared with recent approaches of multi-objective optimizer in solving a multi-objective version of the TEAM 22 benchmark optimization problem with three and eight design variables.
Date of Publication: Feb. 2012