Presentation 2000/5/18
Performance Comparison of Acceptance Probability Functions for Multi-Objective SA
KUBOTANI Hiroyuki, YOSHIMURA Kazuyuki,
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Abstract(in English) A probabilistic local search algorithm called Simulated Annealing(SA)is a useful approximate solution technique for Multi-Objective Optimization Problems. When we use the SA to solve multi-objective optimization problems, we cannot use an acceptance probability function used for single-objective optimization problems. Therefore, several types of the acceptance probability functions to be used for multi-objective SA were proposed. In this paper, we introduce a parameterized acceptance probability function for Multi-objective SA and investigate how the performance of the SA depends on the type of function, based on numerical experiments for two test problems. The numerical results show that the quality of solutions is significantly affected by the type of acceptance probability rule. Weaker type rules give bad solution quality. Moreover, quality of solutions obtained by stronger type rules becomes worse as the number of objective functions increases.
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Keyword(in English) multi-objective optimization / simulated annealing / acceptance probability function
Paper # AI2000-2
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Committee AI
Conference Date 2000/5/18(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Performance Comparison of Acceptance Probability Functions for Multi-Objective SA
Sub Title (in English)
Keyword(1) multi-objective optimization
Keyword(2) simulated annealing
Keyword(3) acceptance probability function
1st Author's Name KUBOTANI Hiroyuki
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name YOSHIMURA Kazuyuki
2nd Author's Affiliation NTT Communication Science Laboratories
Date 2000/5/18
Paper # AI2000-2
Volume (vol) vol.100
Number (no) 88
Page pp.pp.-
#Pages 8
Date of Issue