Effects of Including Single-Objective Optimal Solutions in an Initial Population on Evolutionary Multiobjective Optimization

Yuki Tsujimoto, Yasuhiro Hitotsuyanagi, Y. Nojima, H. Ishibuchi
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引用次数: 2

Abstract

In some multi-objective optimization problems, the search for the optimal solution of each individual objective is much easier than multi-objective optimization. In such a case, it looks a nice idea to search for the single-objective optimal solutions before the execution of multiobjective evolutionary algorithms (MOEAs). In this paper, we examine the effects of including the single-objective optimal solutions in an initial population of MOEAs on their multi-objective search behavior through computational experiments. We use single-machine scheduling problems with two objectives: to minimize the total flow time and the maximum tardiness. The optimal schedules for these two objectives can be easily obtained by sorting the given jobs in ascending order of their processing times and due dates, respectively. Experimental results demonstrate that the inclusion of the optimal solution for each objective (i.e., the inclusion of the two optimal solutions) clearly improves the search ability of NSGA-II. An interesting observation is that its performance is degraded by the inclusion of only the optimal solution for the total flow time.
初始群体中包含单目标最优解对进化多目标优化的影响
在一些多目标优化问题中,寻找单个目标的最优解比寻找多目标优化要容易得多。在这种情况下,在执行多目标进化算法(moea)之前寻找单目标最优解是一个不错的主意。在本文中,我们通过计算实验研究了在初始种群中包含单目标最优解对moea多目标搜索行为的影响。我们使用单机调度问题,有两个目标:最小化总流时间和最大延迟。通过将给定的作业分别按其处理时间和截止日期升序排序,可以很容易地获得这两个目标的最佳时间表。实验结果表明,每个目标的最优解的包含(即两个最优解的包含)明显提高了NSGA-II的搜索能力。一个有趣的观察结果是,它的性能会因只包含总流动时间的最优解而下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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