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A novel elitist multiobjective optimization algorithm: multiobjective extremal optimization

Min-Rong Chen (auminrongchen***at***sjtu.edu.cn)
Yong-Zai Lu (yongzailu***at***yahoo.com)

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 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.

Keywords: Multiobjective optimization; Metaheuristics; Extremal optimization; Self-organized criticality

Category 1: Other Topics (Multi-Criteria Optimization )

Category 2: Combinatorial Optimization (Meta Heuristics )

Category 3: Combinatorial Optimization (Approximation Algorithms )

Citation: European Journal of Operational Research (2007), doi:10.1016/j.ejor.2007.05.008


Entry Submitted: 03/21/2007
Entry Accepted: 03/26/2007
Entry Last Modified: 06/01/2007

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