Abstract
Multi-objective particle swarm optimization (MOPSO), a population-based stochastic optimization algorithm, has been successfully used to solve many multi-objective optimization problems. However, the analysis of algorithm convergence is still inadequate nowadays. In this paper, probability theory is applied to analyze the convergence of the original MOPSO. First, a convergence metric is defined. Afterwards, the global convergence of the original MOPSO is transformed into the convergence of the convergence metric sequence. Finally, the defined convergence metric is utilized to analyze the global convergence of the original MOPSO in terms of probability theory. Our results show that the original MOPSO cannot guarantee global convergence with probability one. Moreover, the analysis of the original MOPSO indicates that the improved vision of the original MOPSO is a global convergence algorithm. The proof of the original MOPSO convergence in this work is new, simple and more effective without specific implementation.
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.
