Title :
Individual Cognitive Parameter Setting Based on Black Stork Foraging Process
Author_Institution :
Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China
Abstract :
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice. Simulation results show this new strategy is superior to other two previous modifications.
Keywords :
cognition; learning (artificial intelligence); particle swarm optimisation; black stork foraging process; cognitive learning factor; cognitive parameter setting; cognitive selection strategy; particle swarm optimization; selection strategies; Acceleration; Animals; Automation; Competitive intelligence; Computational intelligence; Hybrid intelligent systems; Laboratories; Particle swarm optimization; Process design; Stochastic processes; black stork foraging process; cognitive learning factor; particle swarm optimization;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.80