DocumentCode :
3593549
Title :
Improved particle swarm algorithm with a novel local search
Author :
Wang, Mingqian ; Shi, Zhiguo ; Zhao, Haishan
Author_Institution :
Changchun Inst. of Eng. Technol., Changchun, China
Volume :
1
fYear :
2010
Abstract :
This paper presents an improved particle swarm optimization (PSO) algorithm, called IPSO, which employs a novel local search operator. The main idea of IPSO contains three steps. First, we create two trail particles in the local area of a particle. Then, an elitist mechanism is used to select the best one among the current particle and the two trail particles. Third, we replace the current particle with the fittest one. Experimental studies on ten benchmark functions show that the proposed approach IPSO outperforms standard PSO in all test cases.
Keywords :
particle swarm optimisation; search problems; elitist mechanism; improved particle swarm optimization algorithm; novel local search; trail particles; Benchmark testing; Birds; Educational institutions; Evolutionary computation; Heuristic algorithms; Learning systems; Marine animals; Paper technology; Particle swarm optimization; Stochastic processes; evolutionary algorithm; function optimization; particle swarm optimization (PSO);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Future Computer and Communication (ICFCC), 2010 2nd International Conference on
Print_ISBN :
978-1-4244-5821-9
Type :
conf
DOI :
10.1109/ICFCC.2010.5497717
Filename :
5497717
Link To Document :
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