Multi-model function optimization by a new hybrid nonlinear simplex search and particle swarm algorithm

  • Authors:

  • Fang Wang;Yuhui Qiu;Naiqin Feng

  • Affiliations:

  • Faculty of Computer & Information Science, Southwest-China Normal University, Chongqing, China;Faculty of Computer & Information Science, Southwest-China Normal University, Chongqing, China;Faculty of Computer & Information Science, Southwest-China Normal University, Chongqing, China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

A 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.