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Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm
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DOI：10.1016/j.asoc.2012.03.002.png)
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摘要
En 中文
In the present paper, a genetic algorithm for multi-objective optimization problems with max-product fuzzy relation equations as constraints is presented. Since the non-empty feasible domain of such problems is, in general, a non-convex set; the traditional optimization methods cannot be applied. Here, we are presenting a genetic algorithm (GA) to find Pareto optimal solutions for solving such problems observing the role of non-convexity of the feasible domain of decision problem. Solutions are kept within feasible region during the mutation as well as crossover operations. Test problems are developed to evaluate the performance of the proposed algorithm and to determine satisficing decisions. In case of two objectives, weighting method is also applied to find the locus of optimal solutions. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective optimization
Fuzzy relation equations
Max-product composition
Satisficing solutions
Pareto optimal solutions
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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On the relation between equations with max-product composition and the covering problem关于具有最大乘积组成的方程与覆盖问题之间的关系
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