A dynamic population reduction differential evolution algorithm combining linear and nonlinear strategy piecewise functions

Abstract

The population size has a great impact on the performance of the Differential Evolution (DE) algorithm, but in many classic DE algorithms, the population size is usually determined by the user based on the experience value, and it remains unchanged during the evolution process, which greatly affect the performance of DE. To this end, a dynamic population reduction differential evolution algorithm (DPSHADE) that combines linear and nonlinear strategy piecewise functions is proposed. The algorithm uses a dynamic population size reduction method to dynamically adjust the population size during operation, and construct a combination of linear and nonlinear piecewise functions for dynamic scale adaptive adjustment. In this paper, we proposed the DPSHADE algorithm and is compared with the four traditional algorithms in the CEC2017 benchmark set. The experimental results show that DPSHADE performs better in overall performance, which is significant better than the performance of SHADE.