Population reduction with individual similarity for differential evolution

Choosing the appropriate population size for differential evolution (DE) is still a challenging task. Too large population size leads to slow convergence, while too small population size causes premature convergence and stagnation. To solve this problem, a population reduction with individual similarity (PRS) for DE is proposed in this paper. In the PRS, a linear differential decrease method is used to automatically determine the population size required in each generation. At the same time, the current population is divided into two subgroups with equal sizes according to individual similarity, and the individuals that need to be removed are determined from the subgroup with the lowest individual similarity in an effective manner, and thus the convergence is further accelerated without affecting the population diversity. In addition, an elite-oriented strategy is utilized to replace the random selection of individuals in the original mutation strategy of DE, which provides constructive guidance for individual evolution and improves the convergence quality. Five basic DE and six advanced DE algorithms are used to evaluate the effect of PRS, and it is further compared with four improved DE algorithms with population reduction strategy. The experimental results on CEC 2014 benchmark functions show that the proposed PRS can effectively enhance the performance of these five basic DE and six advanced DE algorithms, and is better than the four population reduction strategies.

Automated summary

This paper proposes an individual similarity population reduction strategy and improves the DE algorithm using an elite-oriented strategy. Experimental results show that this method effectively enhances the performance of the DE algorithm.