A novel differential evolutionary algorithm based on population feedback information
Dongfeng Yuan, Qiaofeng An · 2024
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.