서지주요정보
유전자 알고리즘을 이용한 전체/다목적 최적화 알고리즘의 개발 = Development of a global/multicriteria optimization algorithm based on the genetic algorithm
서명 / 저자 유전자 알고리즘을 이용한 전체/다목적 최적화 알고리즘의 개발 = Development of a global/multicriteria optimization algorithm based on the genetic algorithm / 이준배.
저자명 이준배 ; Lee, Jun-Bae
발행사항 [대전 : 한국과학기술원, 1996].
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소장정보

등록번호

8006195

소장위치/청구기호

학술문화관(문화관) 보존서고

DME 96022

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초록정보

A global optimization algorithm and a multicriteria optimization algorithm are developed based on the genetic algorithm(GA). Firstly, a new and efficient global optimization algorithm is developed by combining the new filled function method with the GA. The GA supplies a good initial point for the local optimization procedure based on the filled function and it moves the population toward the better local minima. By introducing the penalty function in the GA step, the local minima previously found is discarded and the tunneling effect can be embodied. In the filled function procedure, the conventional local optimization algorithm is used to obtain a better point than the current minimum. The new filled function is based on the convex homotopy concept. It is a convex combination of the object function and of a special function designed for the global convexity. By minimizing the proposed filled function, we can get a point that is lower than the current minimum or that has the same value as the current minimum or that is included in the lower minimum basin. Secondly, a new method for the generation of a Pareto optimal set is developed for multicriteria optimization problems based on the GA and the global criterion method(GCM). This method results in many Pareto optima almost uniformly distributed on the minimal space and the designer can achieve the highest design degree by selecting the solution among these Pareto solutions. Proposed algorithm utilizes the GA to improve current non-inferior solutions and finally to obtain many Pareto optima at one time. In the GA procedure, the population is separated into two groups, non-inferior points and inferior points, according to the definition of Pareto optimal solution. Non-inferior points have higher probability to survive in the next generation. The GCM is used to obtain the highly evolved points in the current generation. The solution obtained in the GCM procedure is represented as a bit string form and is included in the GA procedure. This information helps the current non-inferior points to converge to minimal space quickly. In the GCM procedure, a conventional local optimization algorithm is used. Since the GA is a global optimization algorithm, however, the presented algorithm has the global property. So the non-convex problem can also be solved by the proposed algorithm. Sharing concept of objective functions is applied to the algorithm for uniformly distributed Pareto optima. The global optimization algorithm is tested by some test typical functions and a 4-bar link synthesis problem. The results show that the present algorithm is superior to the previous algorithms in view of efficiency and reliability. The algorithm also gives a solution suggested by MSPS and a more exact solution than that of MSPS for the 4-bar link synthesis problem. The proposed multicriteria algorithm is also tested by some test functions and a cutting problem. The results show that the proposed algorithm produces a Pareto optimal set in short time by one process. Especially, it gives an almost exact optimal set even for the non-convex test functions.

서지기타정보

서지기타정보
청구기호 {DME 96022
형태사항 ix, 119 p. : 삽도 ; 25 cm
언어 한국어
일반주기 저자명의 영문표기 : Jun-Bae Lee
지도교수의 한글표기 : 이병채
지도교수의 영문표기 : Byung-Chai Lee
수록 잡지명 : "A Global Optimization Algorithm based on the New Filled Function Method". Engineering Optimization
학위논문 학위논문(박사) - 한국과학기술원 : 기계공학과,
서지주기 참고문헌 : p. 74-81
주제 유전자 알고리즘
전체 최적화
다목적 최적화
채움 함수
전체 기준 방법
Genetic Algorithm
Global Optimization
Multicriteria Optimization
Filled Function
Global Criterion Method
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