Abstract
In order to further improve the efficiency of the population diversity in the implementation process of the Particle Swarm Optimization(PSO),an Adaptive PSO(APSO) algorithm based on diversity feedback was proposed. APSO adopted a new population diversity evaluation strategy which enabled the automatic control of the inertia weight with population diversity in the search process to balance exploration and the exploitation's process. In addition,an elite learning strategy was used in the globally best particle to jump out of local optimal solution. It not only ensured the convergence rate of the algorithm,but also adaptively adjusted the search direction to improve the accuracy of solutions. The simulation results on a set of typical test functions verify the validity of APSO.
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.
