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    王学武, 魏建斌, 周昕, 顾幸生. 一种基于超体积指标的多目标进化算法[J]. 华东理工大学学报(自然科学版), 2020, 46(6): 780-791. DOI: 10.14135/j.cnki.1006-3080.20190917001
    引用本文: 王学武, 魏建斌, 周昕, 顾幸生. 一种基于超体积指标的多目标进化算法[J]. 华东理工大学学报(自然科学版), 2020, 46(6): 780-791. DOI: 10.14135/j.cnki.1006-3080.20190917001
    WANG Xuewu, WEI Jianbin, ZHOU Xin, GU Xingsheng. Hypervolume-Based Multi-Objective Evolutionary Algorithm[J]. Journal of East China University of Science and Technology, 2020, 46(6): 780-791. DOI: 10.14135/j.cnki.1006-3080.20190917001
    Citation: WANG Xuewu, WEI Jianbin, ZHOU Xin, GU Xingsheng. Hypervolume-Based Multi-Objective Evolutionary Algorithm[J]. Journal of East China University of Science and Technology, 2020, 46(6): 780-791. DOI: 10.14135/j.cnki.1006-3080.20190917001

    一种基于超体积指标的多目标进化算法

    Hypervolume-Based Multi-Objective Evolutionary Algorithm

    • 摘要: 基于超体积指标的进化算法能够有效地解决多目标优化问题,可以获得收敛性和分布性均较好的解集,但计算复杂度高、程序运行效率低。针对二维和三维的多目标优化问题,提出了一种基于超体积指标的多目标进化算法(MOEA-HV)。利用精确计算种群中个体的独立贡献超体积来指导种群进化,在基于指标的进化算法(IBEA)前对所有种群个体进行非支配排序,提前删除被支配的个体,从而减少个体独立贡献超体积的计算量来提升运行效率,同时与NSGA-Ⅲ算法相结合来优化算法的分布性。实验结果表明,MOEA-HV算法的运行效率更高,且能够获得较好的收敛性和分布性。

       

      Abstract: Hypervolume-based evolutionary algorithms can effectively solve the multi-objective optimization problem and obtain promising solution sets with fast convergence and uniform distribution. However, this kind of algorithms have higher computational complexity and lower programming efficiency. Aiming at the two-dimensional and three-dimensional multi-objective optimization problems, this paper proposes a hypervolume-based multi-objective evolutionary algorithm (MOEA-HV) so that the individuals’ exclusive hypervolume contributions can be accurately calculated to guide the evolution of the whole population. Before the indicator-based evolutionary algorithm(IBEA) being utilized, the proposed algorithm employs non-dominated sorting among all individuals to delete dominated individuals so that the amount of calculation of the individuals’ exclusive hypervolume contributions can be reduced and the operational efficiency can be improved. Meanwhile, other strategies in NSGA-Ⅲ are utilized to optimize the distribution of the proposed algorithm. It is shown via the experiment results that the proposed MOEA-HV has higher efficiency while maintaining the trade-off between the fast convergence and the uniform distribution.

       

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