Recently, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in multiobjective optimization and proposes a new novel elitist multiobjective algorithm, called Multiobjective Extremal Optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems, the proposed approach introduces the Pareto dominance strategy to EO. We also present a new hybrid mutation operator that enhances the exploratory capabilities of our algorithm. The proposed approach is validated using five popular benchmark functions. The simulation results indicate that the proposed approach is highly competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOEO can be considered a good alternative to solve multiobjective optimization problems.
Citation
European Journal of Operational Research (2007), doi:10.1016/j.ejor.2007.05.008