A novel differential evolutionary algorithm based on population feedback information

Dongfeng Yuan; Qixun An

doi:10.1117/12.3049806

8 November 2024 A novel differential evolutionary algorithm based on population feedback information

Dongfeng Yuan, Qixun An

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160M (2024) https://doi.org/10.1117/12.3049806
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

The differential evolution algorithm has rich, successful experience in parameter settings. How to reasonably control strategies and parameters and effectively utilize feedback information from individuals in the population has become a hot topic of concern. In this paper, an adaptive differential evolution algorithm based on population feedback information (TtDE) is proposed. It uses multi-subpopulation selection methods to guide the direction of evolution. TtDE adopts a framework that combines multi-strategies and multi-parameter sets. One set of fixed strategies and parameter selections is the DE/Best/2 and adaptive parameters, and this method can utilize feedback information from individuals in the population effectively. In addition, during population iteration, the mutant subpopulation may contain better information about individuals than the test subpopulation, and the population diversity reasonably avoids early population convergence or stagnation. The proposed TtDE was evaluated on 23 testing functions of the CEC2005 benchmark suite, and the results showed that it is more competitive than multiple classical DE variants.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Dongfeng Yuan and Qixun An "A novel differential evolutionary algorithm based on population feedback information", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160M (8 November 2024); https://doi.org/10.1117/12.3049806

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