Abstract
Bare bone particle swarm optimization (BPSO) possesses self-adapting property and uses fewer parameters resulted in simple implementation and free parameter-tuning. Inevitably, it also tends to converges prematurely, especially for problems with multiple extremes. In this paper, a new method combining global and local learning strategy used in traditional particle swarm optimization (PSO) is devised to improve the performance of the bare bone particle swarm optimization. According to the integration, two variants are proposed. Method is simple and the results are fruitful. Tested on a suite of benchmark functions, unimodal and multimodal functions, justifies the feasibility of the strategy. Both solution quality and convergent speed are better than traditional bare bone particle swarm optimizer.
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.
