DocumentCode :
3274264
Title :
Bare bone particle swarm optimization with integration of global and local learning strategies
Author :
Chen, Chang-Huang
Author_Institution :
Dept. of Electr. Eng., Tungnan Univ., Taipei, Taiwan
Volume :
2
fYear :
2011
fDate :
10-13 July 2011
Firstpage :
692
Lastpage :
698
Abstract :
Bare bone particle swarm optimization (BPSO) possesses self-adapting property and uses fewer parameters resulted in simple implementation and free parameter-tuning. Inevitably, it also tends to converges prematurely, especially for problems with multiple extremes. In this paper, a new method combining global and local learning strategy used in traditional particle swarm optimization (PSO) is devised to improve the performance of the bare bone particle swarm optimization. According to the integration, two variants are proposed. Method is simple and the results are fruitful. Tested on a suite of benchmark functions, unimodal and multimodal functions, justifies the feasibility of the strategy. Both solution quality and convergent speed are better than traditional bare bone particle swarm optimizer.
Keywords :
convergence; particle swarm optimisation; bare bone particle swarm optimization; benchmark function; convergent speed; global learning strategies; local learning strategies; multimodal function; self-adapting property; unimodal function; Benchmark testing; Bones; Cybernetics; Gaussian distribution; Machine learning; Particle swarm optimization; Structural rings; Bare bone particle swarm; Particle swam optimization; Swarm intelligence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
Conference_Location :
Guilin
ISSN :
2160-133X
Print_ISBN :
978-1-4577-0305-8
Type :
conf
DOI :
10.1109/ICMLC.2011.6016781
Filename :
6016781
Link To Document :
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