An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

Abstract

Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm, which has good optimization performance. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we propose an adaptive differential evolution algorithm based on data processing method (ADEDPM). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to-𝑝1 best&𝑝2 best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. Finally, we propose an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve better performance of the algorithm. In order to verify the effectiveness of the proposed algorithm, the ADEDPM is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO, respectively. In the simulation experiment, 21 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed, stability and rank sum test of the algorithm are fully compared. The experimental results show that compared with the five latest optimization algorithms of the same type, the proposed algorithm has strong competitiveness in each test index.