Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Generating of Pareto frontiers using machine learning

기계학습을 이용한 파레토 프런티어의 생성

  • 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)
  • 윤예분 (간사이대학교 환경 도시공학부) ;
  • 정나영 (부경대학교 통계학과) ;
  • 윤민 (부경대학교 통계학과)
  • Received : 2013.03.22
  • Accepted : 2013.05.02
  • Published : 2013.05.31
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Abstract

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.

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.

Keywords

References

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