An adaptive differential evolution framework based on population feature information

Differential Evolution (DE) is an effective global optimization algorithm, and many existing adaptive variants of it have been proposed to solve engineering problems. It is well known that population feature information that refers to some mathematical statistic feature information of all individuals in the dimension of decision space, and it can reflect the fea-tures of the problem to be solved. However, the population feature information has not been fully utilized by DE's adaptive variants. As a result, those adaptive variants do not obtain promising performance in optimizing nonlinear, non-differentiable and non -separable multi-modal problems. To make adequate extraction and effective use of popu-lation feature information, we propose an adaptive differential evolution framework based on population feature information in this paper, named PFI for short. In the PFI framework, the population feature information consists of the standard deviation of fitness value and the sum of standard deviation of each dimension of population. Besides, population feature information archive is designed to store the population feature information and success parameters, and the utilization mechanism of population feature information assigns his-torical success parameters with high population feature similarity to the current corre-sponding population. Four widely used mutation strategies of DE are incorporated into the PFI framework to evaluate its performance by optimizing CEC2005, CEC2015, CEC2020 benchmark functions and two real world applications to verify the performance of the PFI framework. Experiment results have demonstrated that PFI framework can sig-nificantly improve the performance of 4 popular mutation strategies of DE.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes an adaptive differential evolution framework called PFI based on population feature information to improve the performance of DE in optimizing nonlinear, non-differentiable, and non-separable multi-modal problems.