DocumentCode :
2742333
Title :
Personal Best Oriented Constriction Type Particle Swarm Optimization
Author :
Chen, Chang-Huang ; Yeh, Sheng-Nian
Author_Institution :
Dept. of Electr. Eng., Tung Nan Inst. of Technol., Taipei
fYear :
2006
fDate :
7-9 June 2006
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, a new search strategy for constriction type particle swarm optimization is presented. The modification is based on the observation that personal past best experience is helpful for searching optimal result. As a result, instead of moving particle to the vicinity of current position, by letting the particle to explore the proximity of personal best position, a great improvement in computation efficiency and quality is achieved. The results are verified through testing on benchmark functions. The advantage of this new scheme is that no extra mathematic operation is introduced compared to those modifications proposed in literature
Keywords :
particle swarm optimisation; benchmark functions; constriction type particle swarm optimization; personal best position; search strategy; swarm intelligence; Acceleration; Benchmark testing; Convergence; Evolutionary computation; Genetic mutations; Mathematics; Optimization methods; Particle swarm optimization; Random number generation; optimization; particle swarm optimization; swarm intelligence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2006 IEEE Conference on
Conference_Location :
Bangkok
Print_ISBN :
1-4244-0023-6
Type :
conf
DOI :
10.1109/ICCIS.2006.252300
Filename :
4017859
Link To Document :
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