An External Selection Mechanism for Differential Evolution Algorithm. (4th April 2022)

• Record Type: Journal Article
• Authors: Zhang, Haigang; Wang, Da
• Other Names: Zhou Miaolei Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-04-04
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/4544818
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The procedures of differential evolution algorithm can be summarized as population initialization, mutation, crossover, and selection. However, successful solutions generated by each iteration have not been fully utilized to our best knowledge. In this study, an external selection mechanism (ESM) is presented to improve differential evolution (DE) algorithm performance. The proposed method stores successful solutions of each iteration into an archive. When the individual is in a state of stagnation, the parents for mutation operation are selected from the archive to restore the algorithm's search. Most significant of all, a crowding entropy diversity measurement in fitness landscape is proposed, cooperated with fitness rank, to preserve the diversity and superiority of the archive. The ESM can be integrated into existing algorithms to improve the algorithm's ability to escape the situation of stagnation. CEC2017 benchmark functions are used for verification of the proposed mechanism's performance. Experimental results show that the ESM is universal, which can improve the accuracy of DE and its variant algorithms simultaneously.