Abstract
Abstract In recent years, a variety of multi-objective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, the performance of most existing MOEAs is sensitive to the Pareto front (PF) shapes of the problem to be solved, and it is difficult for these algorithms to manage diversity on various types of Pareto fronts (PFs) effectively. To address these issues, this paper proposes an evolutionary algorithm based on diversity ranking method for multi-objective and many-objective optimization. The proposed evolutionary algorithm introduces reference vector adaptation method to solve different shapes of Pareto fronts, and proposes the diversity ranking method to manage diversity. The extensive experimental results demonstrate that the proposed algorithm can solve various types of Pareto fronts, surpassing several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
