Multi-objective strength Pareto chaotic differential evolution algorithm

A multi-objective strength Pareto chaotic differential evolution algorithm(SPCDE) is proposed. Firstly, the chaotic initialization based on the Tent map is adopted to initialize the population. A truncation crowding operation based on a uniform crowding mechanism and a chaotic substitution operation are introduced to the environmental selection operation of the population. Then, the differential mutation operation is operated based on a differential mutation strategy with changing scaling factor, and the mutation individuals are obtained by computing the dominance relation. Finally, the evolutionary selection operation is operated and the offspring individuals are obtained by the dominance relation computation and the environmental selection operation. The operations above mentioned not only enhance the convergence performance of the proposed algorithm, but also maintain the uniformity of the Pareto optimal solution. Numerical experiment results show the effectiveness of the proposed algorithm.