Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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기계학습을 이용한 파레토 프런티어의 생성

Generating of Pareto frontiers using machine learning

  • 윤예분 (간사이대학교 환경 도시공학부) ;
  • 정나영 (부경대학교 통계학과) ;
  • 윤민 (부경대학교 통계학과)
  • Yun, Yeboon (Faculty of Environmental and Urban Engineering, Kansai University) ;
  • Jung, Nayoung (Department of Statistics, Pukyong National University) ;
  • Yoon, Min (Department of Statistics, Pukyong National University)
  • 투고 : 2013.03.22
  • 심사 : 2013.05.02
  • 발행 : 2013.05.31
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초록

진화 알고리즘 계산 지능을 이용한 예측 방법이 다목적 최적화 문제에서 많이 이용되고 있고, 이러한 방법들은 많은 근사 파레토 최적해들을 좀 더 정확하게 생성하기 위해서 개선되고 있다. 본 논문은 다목적 최적화 문제에서 서포트 벡터기계를 이용하여 근사 파레토 프런티어를 찾는 방법을 제안한다. 또한 제안된 방법과 진화 알고리즘을 결합한 것이 파레토 프런티어를 더 잘 근사시킨다는 것과 두 개혹은 세 개의 목적함수를 가진 의사결정은 제안된 방법으로 파레토 프런티어를 시각화한 것에 근거하여 더 쉽게 수행된다는 것을 보인다. 마지막으로 몇 개의 수치예제를 통해 제안된 방법의 효율성에 대해 보일 것이다.

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

키워드

참고문헌

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