Differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solutio...
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Differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. 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 proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). 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 insuf-ficient 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 strat-egy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising can-didate solution region, and indirectly increases the population diversity of the algo-rithm. We also proposed an adaptive parameter control method, which can effec-tively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are
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