Abstract
In recent years, many hybrid metaheuristic approaches have been proposed to solve multiobjective optimization problems (MOPs). In this paper, we present a novel multiobjective algorithm, so-called MOPSOEO, which combines particle swarm optimization (PSO) with extremal optimization (EO) to solve MOPs. The hybrid approach takes full advantage of the exploration ability of PSO and the exploitation ability of EO, which can overcome the premature convergence of PSO when it is applied to MOPs. The proposed approach is validated by using five benchmark functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Experimental results indicate that the approach is highly competitive with the state-of-the-art evolutionary multiobjective algorithms, and thus, MOPSOEO can be considered a viable alternative to solve MOPs.
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