This study investigates the performance and efficiency of using variant Differential Evolution (DE) strategies on two classical optimization problems along with fuzzy coefficients. Two classical problems considered in this study are fuzzy transportation and fuzzy linear programming. The fuzzy coefficients in the objective function of the problems are represented by fuzzy numbers. The signed-distance ranking method is used to rank these fuzzy numbers during the evaluation and the selection processes of the DE algorithm for solving the fuzzy optimization problems. The success of DE in solving a specific optimization problem crucially depends on appropriately choosing mutation strategies and their associated control parameter values. There are several variants of mutation constituting several corresponding DE strategies can be used on the optimization problem with fuzzy coefficients. The performance of using five variant mutation strategies on two classical problems are investigated and compared. The experiments conducted show that DE/best/1 strategy generally outperforms the other variant DE strategies in terms of efficiency and convergence on the fuzzy transportation and the fuzzy linear programming problems.
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