Multi-objective differential evolution algorithm based on fast sorting and a novel constraints handling technique

In this paper, an improved multi-objective differential evolution algorithm is proposed to solve constraints in multi-objective optimization. Research has shown that the information of infeasible solutions is also important and can help the algorithm improve the convergence and diversity of solutions. A novel constraint handling method is introduced to ensure that a certain number of good infeasible solutions will be kept in the procedure of evolution to guide the search of the individuals. The proposed method is compared with two other constrained multi-objective differential evolution algorithms and the results show that the proposed method is competitive.

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