Best resolution and post-optimal analysis in co-operation of multi-objective optimization and multi-objective evolutionary algorithm (An application to multiple car structures design toward energy and material conservation)

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  • 多目的最適化と多目的進化手法の協調援用による最良決定と事後解析（省エネルギ・省資源化に向けた複数車体構造の多目的設計への適用）

Abstract

<p>As represented by energy and/or environmental issues, modern technologies are facing with a lot of difficult problems that must be solved urgently for sustainable development. Concerning with anyone of those problem-solving, however, we often come across such a situation that requires us to consider multiple goals conflicting with each other. To deal with such complicated and difficult problem, multi-objective optimization has been highly required and supported agile and flexible decision-making. Accordingly, we developed a novel method termed MOON2R/MOON2 and demonstrated its effectiveness through various applications. To enhance its usefulness, in this paper, we have proposed a unique procedure for multi-objective optimization and its post-optimal analysis. Actually, it is deployed in co-operation with our elite-induced multi-objective evolutionary algorithms (MOEA) and a new idea named downsizing NSGA-II together with a technique to solve single-objective optimization problem using MOEA. After preliminarily examining some properties of the idea, a practical benchmark problem on multiple car structure design has been solved to discuss the significance of the proposed idea. Eventually, the proposed idea makes multi-objective optimization more practical toward recent qualified decision-making.</p>

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