서지주요정보
Development of tabu search-based heuristics for solving exact optimal design and clustering problems = 최적실험계획문제와 집락문제의 해결을 위한 타부탐색에 근거한 발견적 해법의 개발
서명 / 저자 Development of tabu search-based heuristics for solving exact optimal design and clustering problems = 최적실험계획문제와 집락문제의 해결을 위한 타부탐색에 근거한 발견적 해법의 개발 / Joo-Sung Jung.
저자명 Jung, Joo-Sung ; 정주성
발행사항 [대전 : 한국과학기술원, 1996].
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8006403

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학술문화관(문화관) 보존서고

DIE 96003

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DIE 96003 c. 2

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초록정보

This thesis is concerned with developing algorithms for solving exact optimal design and clustering problems in statistics. To alleviate the possibility of being trapped at a local optimum, which is frequently the case in the existing algorithms, we developed Tabu search(TS)-based algorithms for both single- and multi-objective problems. We first developed TS-based algorithms for the exact optimal design and clustering problems with a single objective. We further developed TS-based direct algorithms(TSDA) which can handle multi-objective problems. TSDA is based on the difinition of Pareto-efficient solutions. Computational experiments for single-objective problems indicate that for the exact optimal problems, TS-based algorithms perform better than simulated annealing(SA) and Fedorov algorithm(FEA) in terms of the number of successes per unit time which may be considered as a combining measure of solution quality and computational efforts required, although SA is better in terms of solution quality and FEA requires less computing than the others. For the clustering problem, SA generally performs better than TS and K-Means algorithm in terms of solution quality. The K-Means algorithm requires less computing time than the others. In terms of the number of successes per unit time for complex problems, TS performs better than the others. The experimental results for multi-objective problems show that for the exact optimal design problem, TSDA is far superior to the existing algorithm. However, for the clustering problem, TSDA tends to require more computing time than the other algorithm as the number of criteria increases. In summary, TS-based algorithm are believed to be useful alternatives to the existing algorithms for solving the exact optimal design and the clustering problems in statistics. It is recommended that future research be directed towards improving the present TSDA with respect to the required amount of computing time for the clustering problem with many criteria.

서지기타정보

서지기타정보
청구기호 {DIE 96003
형태사항 [v], 179 p. : 삽도 ; 26 cm
언어 영어
일반주기 Appendix : A, Performances of TS with respect to parameter values for the D-optimal design problems. - B, Data sets for single-objective clustering problems. - C, Performances of TS with respect to parameter values for single-objective clustering problems. - D, Data sets for multi-objective clustering problems
저자명의 한글표기 : 정주성
지도교수의 영문표기 : Bong-Jin Yum
지도교수의 한글표기 : 염봉진
학위논문 학위논문(박사) - 한국과학기술원 : 산업공학과,
서지주기 Reference : p. 128-131
주제 Exact Optimal Design
Clustering
Tabu Search
Multiple Objectives
최적실험계획
집락
타부탐색
다목적
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