다목적 유전자 알고리즘에 있어서 적합도 평가방법과 대화형 의사결정법의 제안

• 영문명: Development of Fitness and Interactive Decision Making in Multi-Objective Optimization
• 발행기관: 한국산업경영시스템학회
• 저자명: 윤예분 박동준 윤민
• 간행물 정보: 『산업경영시스템학회지』제45권 제4호, 109~117쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-ob- jective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

목차

1. 서 론

2. 성취도 수준에 따른 적합성 평가를 위한 제안

3. 수치 예제들의 결과

4. 만족도에 기반한 의사결정의 제안

5. 결 론

키워드

Multi-Objective Optimization Pareto Solution Achievement Satisfaction Genetic Algorithms