Most of the real world optimization problems are multi-objective in nature. Recently, Evolutionary algorithms are gaining popularity for solving Multi-Objective Optimization Problems (MOOPs) due to their inherent advantages over traditional methods. In this paper, Differential Evolution (an evolutionary algorithm that is significantly faster and robust for optimization problems over continuous domain) is extended for solving MOOPs and we call this extended algorithm as Non-dominated Sorting Differential Evolution (NSDE). The proposed algorithm is applied successfully to two different benchmark test problems. Also, the effect of various key parameters on the performance of NSDE is studied. A high value of crossover constant (≌1) and a value of 0.5 for scaling factor are found suitable for both the problems.
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