Abstract
In the presented article, a new multi-objective PSO algorithm with a novel adaptive global best selecting strategy (AD-MOPSO) has been proposed. The strategy which compromises global and local searching ability selects gBest based on particles’ sigma values and density values. A particle’s density value is evaluated using the crowding angle of each particle in polar coordinates. An adaptive tuning coefficient is designed to maintain a good trade-off between convergence and diversity performance of the algorithm. AD-MOPSO is compared with other two newly developed MOPSO algorithms on a set of well-designed benchmark functions. Results indicate that the new algorithm shows better convergence and diversity performance than other two algorithms.
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.
