Constrained Differential Evolution Algorithm with Dynamic Elite Learning
ZHOU Xinyu1,2, JIANG Jinfeng1, GAO Weifeng3, WANG Hui4, PENG Hu5
1. School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022; 2. Key Laboratory of Intelligent Information Processing and Emotional Computing in Jiangxi Province, Jiangxi Normal University, Nanchang 330022; 3. School of Mathematics and Statistics, Xidian University, Xi'an 710071; 4. School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang 330099; 5. School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005
Abstract：Constrained differential evolution(CDE) algorithm is an effective way to solve constrained optimization problems. However, the existing research mainly focuses on constraint handling techniques, while the differential evolution algorithm itself is neglected, resulting in some problems, including unbalanced exploration and exploitation capabilities, and a low survival rate of offspring individuals of feasible solutions. To address these issues, a dynamic elite learning strategy is designed to improve the performance of the CDE algorithm. In this strategy, the individuals in the population are divided into ordinary feasible solutions, elite feasible solutions and infeasible solutions, and individualized mutation operators are adopted for each of these three types of individuals to balance the exploration and exploitation capabilities. Meanwhile, the elite feasible solutions are introduced to improve the classical mutation operators, thereby increasing the survival rate of offspring of feasible solutions. According to the characteristics of infeasible solutions, a fine-tuned feasibility rule is also designed as a constraint handling technique to better guide the population into the feasible region. Experiments on CEC2006, CEC2010 and CEC2017 test sets as well as three real-world engineering optimization problems demonstrate that the proposed algorithm achieves superior performance compared with six state-of-art constrained optimization evolutionary algorithms.
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2025, Vol. 38 
