Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm

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• 作者：Antika Thapar^a ; ^{antikathapar@gmail.com} ; Dhaneshwar Pandey^a ; ^{dpdr@rediffmail.com} ; S.K. Gaur^b ; ^{santfoedei@gmail.com}
• 关键词：Multi-objective optimization ; Fuzzy relation equations ; Max-product composition ; Satisficing solutions ; Pareto optimal solutions
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 期刊代码：9_15684946
• 类别：cp
• 出版时间：August, 2012
• 卷：12
• 期：8
• 页码：2178-2187
• 文件大小：1368 K

摘要

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 鈥淧areto optimal solutions鈥?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.