Constrained multi-objective differential evolution algorithm with ranking mutation operator. (1st December 2022)

• Record Type: Journal Article
• Authors: Yu, Xiaobing; Luo, Wenguan; Xu, WangYing; Li, ChenLiang
• Is Part Of: Expert systems with applications. Volume 208(2022)
• Publication Date: 2022-12-01
• Subjects: CMOPs -- Differential evolution -- Mutation operator -- Ranking; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118055
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: There are feasible and infeasible solutions in Constrained Multi-objective Optimization Problems (CMOPs). The feasible solutions with lower rank should be given more chances to generate offspring, while infeasible and worse solutions with higher rank should have fewer chances in these CMOPs. A constrained multi-objective Differential Evolution (DE) algorithm is developed by considering the selection pressure. The population is ranked based on the non-dominated crowd sort and constrained dominated principle. Then, a tournament operator is designed to extend the conventional mutation operator to boost the exploitation. The performances of the proposed algorithm are assessed on nineteen benchmark functions and industrial applications. Five representative algorithms are selected to make comparisons. The experiments have demonstrated that the algorithm can find well-distributed Pareto front, and the result of the performance indicator is superior.