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A simulated annealing-based multiobjective optimization algorithm: AMOSA

delete2008-06-01
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OA
AI
S
Sanghamitra Bandyopadhyay *
S
Sriparna Saha
U
Ujjwal Maulik
K
Kalyanmoy Deb
DOI:10.1109/TEVC.2007.900837delete
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摘要

摘要

En 中文
This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the. proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.
Keyword:
amount of domination
archive
clustering
multi-objective optimization (MOO)
Pareto-optimal (PO)
simulated annealing (SA)

期刊

IEEE Transactions on Evolutionary Computation 封面图
IEEE Transactions on Evolutionary Computation
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12
论文数:
1.8K
被引数:
2.4W

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indian statistical institute kolkata
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Jadavpur University
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Indian Statistical Institute
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