DEDF: An Enhanced Differential Evolution Algorithm with Dynamic-selection Framework in IIOT
Zhou Zhou, Fangmin Li u, Huazhong Liu u · 2023
To solve the problems of slow convergence and limited prediction ability of the Differential Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic-selection framework (denoted by DEDF) is proposed. DEDF develops a trusted framework containing five mutation strategies to realize the dynamic selection of mutation strategies. On the basis of the framework, the mutation factor, crossover factor, and local exit strategy are improved to balance the algorithm’s local search and global search ability. A series of tests on the function set CEC2017 has been performed, and the findings show that compared with other benchmark algorithms, the DEDF has advantages in convergence speed and accuracy. The proposed algorithm DEDF can be effectively leveraged to address multi-objective optimization issues in IIOT.