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Volume 6 Issue 2
Mar.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Yang Yu, Shangce Gao, Yirui Wang and Yuki Todo, "Global Optimum-Based Search Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379-394, Mar. 2019. doi: 10.1109/JAS.2019.1911378
Citation: Yang Yu, Shangce Gao, Yirui Wang and Yuki Todo, "Global Optimum-Based Search Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379-394, Mar. 2019. doi: 10.1109/JAS.2019.1911378

Global Optimum-Based Search Differential Evolution

doi: 10.1109/JAS.2019.1911378



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  • 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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