Advances in differential evolution for solving multiobjective optimization problems
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Differential Evolution (DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. This paper proposed a multiobjective differential evolutionary algorithm based on opposite operation. Firstly, in the initialization of the algorithm, the opposite points of randomly generated individuals are calculated in order to make the initial population better. Secondly, the opposite operation has also been used for candidate solutions according to the number of the nondominated individuals generated by DE dynamically. In doing so, the convergence rate of DE can be improved. Experiment results confirm the effectiveness of the proposed algorithm.