Abstract
AbstractA new hybrid Particle Swarm Optimization (PSO) algorithm is proposed based on the Nonlinear Simplex Search (NSS) method. At late stage of PSO, when the most promising regions of solutions are fixed, the algorithm isolates particles that are very close to the extrema, and applies the NSS method to them to enhance local exploitation searching. Explicit experimental results on famous benchmark functions indicate that this approach is reliable and efficient, especially on multi-model function optimizations. It yields better solution qualities and success rates compared to other published methods.KeywordsParticle Swarm OptimizationParticle SwarmParticle Swarm Optimization AlgorithmHybrid Particle Swarm OptimizationParticle Swarm AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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.
