進化型多目的最適化における探索履歴を活用した局所解脱出と集中探索メカニズム

application/pdfIn this paper, a new local search approach using a search history in evolutionary multi-criterion optimization (EMO) is proposed. This approach was designed by two opposite mechanisms (escaping from local optima and convergence search) and assumed to incorporate these into an usual EMO algorithm for strengthening its search ability. The main feature of this approach is to perform a high efficient search by changing these mechanisms according to the search condition. If the search situation seems to be stagnated, escape mechanism would be applied for shifting search point from this one to another one. On the other hand, if it observes no sign of the improvement of solutions after repeating this escape mechanism for a fixed period, convergence mechanism is applied to improve the quality of solution through an intensive local search. This paper presents a new approach, called “escaping from local optima and convergence mechanisms based on search history - SPLASH -”. Experimental results showed the effectiveness of SPLASH and the workings of SPLASH’s two mechanisms using WFG test suites

MOEA/Dにおける集約関数の動的制御に関する検討

application/pdf代表的な進化型多目的最適化手法の１つに MOEA/D(A Multiobjective Evolutionary Algorithm Based on Decomposition) における集約関数の動的な制御方法について検討を行った.集約関数はそれぞれ異なる特徴,指向性を持っているため,探索途中の集約関数の単純な切り替えは結果として探索が非効率となるが,本研究ではそれぞれの集約関数に適したアーカイブを常に保持し,集約関数の切替と同時に探索に利用するアーカイブも切り替えることでその非効率性の克服を目指した.代表的なテスト関数を用いた数値実験を通して,提案手法の有効性を確認することができた

Threading Dislocations in InGaAs CaP Layers with InGaAs Graded Layers Grown on Si Substrates

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