Dynamic Population Structures-Based Differential Evolution Algorithm

Abstract

The coordination of population structure is the foundation for the effective functioning of evolutionary algorithms. An efficient population evolution structure can guide individuals to engage in successful and robust exploitative and exploratory behaviors. However, due to the black-box property of the search process, it is challenging to assess the current state of the population and implement targeted measures. In this paper, we propose a dynamic population structures-based differential evolution algorithm (DPSDE) to uncover the real-time state of population continuous optimization. According to the exploitation and exploration state of population, we introduce four structural modules to address the premature convergence and search stagnation issues of the current population. To effectively utilize these modules, we propose a real-time discernment mechanism to judge the population's current state. Based on the feedback information, suitable structural modules are dynamically invoked, ensuring that the population undergoes continuous and beneficial evolution, ultimately exploring the optimal population structure. The comparative outcomes with numerous cutting-edge algorithms on the IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark functions and 2011 real-world problems verify the superiority of DPSDE. Furthermore, parameters, population state, and ablation study of modules are discussed.