Abstract
In recent years, multi-objective optimization problems (MOOPs) have gained a lot of attentions from the community of evolutionary algorithm since many real-world optimization problems would involve multiple objective functions. In this paper, a species-based multi-objective genetic algorithm (SMOGA) that hybridizes a species method, which was initially designed in GA for multi-modal problems, with the algorithm mechanism of NSGA-II, which was one of well-known MOGAs, is proposed for MOOPs. In order to examine the performance of the proposed algorithm, experiments were carried out to investigate the strength and weakness of SMOGA on a series of test MOOPs in comparison with NSGA-II.
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.
