DocumentCode :
2823959
Title :
A novel memetic algorithm based on the comprehensive learning PSO
Author :
Ni, JiaCheng ; Li, Li ; Qiao, Fei ; Wu, QiDi
Author_Institution :
Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
8
Abstract :
A memetic algorithm MCLPSO based on the comprehensive learning PSO (CLPSO) is presented in this study. In MCLPSO, a chaotic local search operator is used and a Simulated Annealing (SA) based local search strategy is developed by combining the cognition-only PSO model with SA. The memetic scheme can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the multimodal functions compared with some other variants of PSO including CLPSO.
Keywords :
cognition; evolutionary computation; learning (artificial intelligence); particle swarm optimisation; search problems; simulated annealing; MCLPSO; chaotic local search operator; cognition-only PSO model; comprehensive learning PSO; comprehensive learning strategy; fine-grained local search; memetic algorithm; multimodal functions; simulated annealing based local search strategy; Cognition; Convergence; Mathematical model; Memetics; Particle swarm optimization; Search problems; Topology; PSO; SA-based local search; chaotic local search; comprehensive learning strategy; memetic algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
Type :
conf
DOI :
10.1109/CEC.2012.6256632
Filename :
6256632
Link To Document :
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