A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization

Abstract

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 numerical multiobjective optimization and proposes a new novel elitist (1 + λ ) multiobjective algorithm, called multiobjective extremal optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems , the Pareto dominance strategy is introduced to the fitness assignment of the proposed approach. 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 numerical multiobjective optimization problems.