DocumentCode :
2742529
Title :
Self-learning Particle Swarm Optimization Based on Environmental Feedback
Author :
Cai, Xingjuan ; Cui, Zhihua ; Zeng, Jianchao ; Tan, Ying
Author_Institution :
Taiyuan Univ. of Sci. & Technol., Taiyuan
fYear :
2007
fDate :
5-7 Sept. 2007
Firstpage :
570
Lastpage :
570
Abstract :
Particle swarm optimization (PSO) simulates the behaviors of birds ocking and poundsh schooling. However, its biological background does not concern the environmental affection. Inspired by the interaction between environment and individuals, a new version - self-learning particle swarm optimization based on environmental feedback (SL-PSO), is proposed, in which two self-learning strategies are designed so that each particle adjusts its moving direction according to the feedback information from the environment. Furthermore, a mutation operator is introduced to avoid premature convergence phenomenon. Simulation results show the proposed algorithm is effective and efpoundcient.
Keywords :
particle swarm optimisation; environmental feedback; mutation operator; premature convergence phenomenon; self-learning particle swarm optimization; self-learning strategies; Biological system modeling; Birds; Computational modeling; Computer applications; Computer simulation; Convergence; Educational institutions; Genetic mutations; Particle swarm optimization; State feedback;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
Type :
conf
DOI :
10.1109/ICICIC.2007.512
Filename :
4428212
Link To Document :
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