A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization
A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization
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DOI：
10.1016/j.ejor.2007.05.008
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
Min-Rong Chen;Yongzai Lu
中科院分区：
文献类型：
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作者：
Min-Rong Chen;Yongzai Lu
近年来，极值优化（EO）方法被成功地应用于解决一些NP难的组合优化问题。本文研究了极值优化算法及其在数值多目标优化中的应用，提出了一种新的精英（1+λ）多目标优化算法，称为多目标极值优化（MOEO）。为了将进化算法推广到求解多目标优化问题，将Pareto优势策略引入到该算法的适应度分配中。我们还提出了一个新的混合变异算子，提高了我们的算法的探索能力。使用五个流行的基准测试函数验证所提出的方法。仿真结果表明，该方法是非常有竞争力的国家的最先进的多目标进化算法。因此，MOEO可以被认为是一个很好的替代解决数值多目标优化问题。
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.