進化型多目的マルチタスク最適化手法におけるタスク間交叉時の親個体が探索性能に与える影響

複数の多目的最適化問題（タスク）を同時に解く進化型多目的マルチタスク最適化手法（Evolutionary Multiobjective Multitasking: EMOMT）に関する研究が近年盛んに行われている．EMOMTの特徴として，タスク毎に個体群を生成して探索を行い，探索途中で異なるタスクを解く個体間で交叉を行う点が挙げられる．タスク間の交叉により生成された子個体を各タスクの個体群に追加することでEMOMTの探索性能の向上が報告されている．タスク間の交叉において，決定変数空間における親個体の位置が極端に離れると子個体の生成領域が非常に大きくなるなど，決定変数空間における親個体の位置は生成する子個体，さらに次世代の個体群に大きな影響を及ぼすと考えられるが，十分に調査されていない．そこで本研究では決定変数空間内の親個体の位置に着目し，親個体の選択方法が探索性能へ与える影響を調査する．

Recently, evolutionary multiobjective multitasking (EMOMT) that solves multiple multiobjective optimization problems (i.e., multiple tasks) in parallel using evolutionary computation has been actively studied. In evolutionary multitasking, each task has a population to be optimized by evolutionary computation. The main feature of EMOMT is that offspring individuals are generated by not only intra-task crossover but also inter-task crossover. Previous studies show that adding offspring individuals generated by inter-task crossover to each population improves the search ability of EMOMT. In inter-task crossover, the position of parent individuals in the decision space affects the generated offspring individuals and the next population. For example, when the position of parent individuals is extremely far away in the decision space, the generation area of offspring individuals becomes very large. However, selection of appropriate parents in inter-task crossover is not well-studied. In this paper, we focus on the similarity of individuals between two populations (i.e., two tasks) in the decision space and examine the effects of different parent selection schemes on the search performance of EMOMT.