A differential evolution algorithm based on accompanying population and piecewise evolution strategy

Abstract

Differential evolution (DE) is a simple and effective stochastic search algorithm, but its convergence speed and population diversity often decline catastrophically with the evolution process. In this paper, a differential evolution algorithm based on accompanying population and piecewise evolution strategy (APPDE) is proposed. The accompanying population is used to store suboptimal solutions, and its initialization, reinitialization and renewal mechanisms are designed to maintain the characteristics of suboptimal solutions and enhance the population diversity. The mutation operators are improved based on the accompanying population to balance the exploration and exploitation ability. In view of the phenomenon that the evolution speed slows down or even stagnates, the mutation strategies and control parameters are optimized by combining with piecewise evolution. The performance of APPDE is evaluated on CEC2014, CEC2015 and CEC2017 benchmark problem suites, and compared with the state-of-the-art optimization algorithms. The results show that APPDE has better performance than the competitive algorithms.