Solving fuzzy optimization problems by evolutionary algorithms
Information Sciences: an International Journal
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Solving the constrained p-center problem using heuristic algorithms
Applied Soft Computing
Exact and heuristic procedures for solving the fuzzy portfolio selection problem
Fuzzy Optimization and Decision Making
Solving fuzzy p-hub center problem by genetic algorithm incorporating local search
Applied Soft Computing
A hybrid metaheuristic approach for the capacitated p-median problem
Applied Soft Computing
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We propose a genetic algorithm for the fuzzy p-median problem in which the optimal transport cost of the associated crisp problem is unknown. Our algorithm works with two populations: in one, the solutions with a better crisp transport cost are favored by the selection criterion, whereas in the second one, solutions with a better fuzzy satisfaction level are preferred. These populations are not independent. On the contrary, the first one periodically invades the second one, thus providing new starting points for finding fuzzy improvements. Our computational results also reveal the importance of choosing adequate functions for selecting the parents. Our best results are obtained with functions which are more sensitive to both objectives (crisp and fuzzy) and by increasing the invasion and mutation rates. We compare these results with other heuristic procedures.