Abstract
ABSTRACT In this paper, a hierarchical particle swarm optimization (HPSO) is proposed to improve the premature convergence in the PSO approach. In the proposed HPSO approach, all particles are arranged in a regular tree structure and move up or down in the tree based on their fitness value. For the velocity update of each particle, it depends on the position of each particle in the tree. Besides, a mutation operator is added into the proposed HPSO approach. Consequently, the diversity of the population increases so that the HPSO approach can improve the premature convergence in the PSO approach. Finally, several benchmark functions for optimization problems are utilized to illustrate the effectiveness of the proposed HPSO approach to improving the premature convergence.
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.
