Selection of Initial Solutions for Local Search in Multiobjective Genetic Local Search

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  • Hitotsuyanagi Yasuhiro
    Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
  • Wakamatsu Yoshihiko
    Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
  • Nojima Yusuke
    Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
  • Ishibuchi Hisao
    Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

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  • 多目的遺伝的局所探索アルゴリズムにおける局所探索適用個体の選択
  • タモクテキ イデンテキ キョクショ タンサク アルゴリズム ニ オケル キョクショ タンサク テキヨウ コタイ ノ センタク

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Abstract

In this paper, we propose a new selection scheme of initial solutions for the local search of a multiobjective genetic local search (MOGLS) algorithm. The MOGLS algorithm is the hybridization of an evolutionary multiobjective optimization (EMO) algorithm and local search. It is shown that the MOGLS algorithm has higher search ability than pure EMO algorithms. In the conventional MOGLS algorithm, the local search method is applied to the offspring population generated by the genetic operators. However, the generated offspring population often includes poor individuals because the genetic operators involve some random procedures and allow the generation of inferior offspring. The basic idea of our approach is to apply local search to the parent population. Thus our approach can apply local search to better solutions than the original MOGLS algorithm on average. Through computational experiments, we show that our approach improves the search ability of the MOGLS algorithm. <br>

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