期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 6, 期 2, 页码 379-394出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2019.1911378
关键词
Differential evolution (DE); global optimum; memetic algorithm
资金
- JSPS KAKENHI [JP17K12751, JP15K00332]
In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution (DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.
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