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-objective 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.

1. 서 론
2. 성취도 수준에 따른 적합성 평가를 위한 제안
3. 수치 예제들의 결과
    3.1 DTLZ1 문제 [1]의 수치 예제
    3.2 Cantilever Welded Beam Design[18] 예제
4. 만족도에 기반한 의사결정의 제안
    4.1 만족도 기반의 의사결정방법 절차
5. 결 론
References

• Yeboon Yun(간사이대학교 환경도시공학부) | 윤예분
• Dong Joon Park(부경대학교 통계 데이터사이언스전공) | 박동준
• Min Yoon(부경대학교 응용수학과) | 윤민 Corresponding Author