DocumentCode :
2558271
Title :
New Particle Swarm Optimization algorithm for knapsack problem
Author :
Ouyang, Ling ; Wang, Dongyun
Author_Institution :
Electr. Dept., Zhongyuan Univ. of Technol., Zhengzhou, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
786
Lastpage :
788
Abstract :
In this paper it proposes an improved Particle Swarm Optimization (PSO) algorithm for the knapsack problem. The new algorithm is based on the standard PSO algorithm for overcoming the shortcomings that standard PSO traps into local optima easily and has a low convergence accuracy. When the load-bearing quantity of the knapsack is exceeded, the fitness will be sit zero. When the best position of the individual particle is the same with the best position of the population, the particle´s position will be reinitialized. The simulation shows that the improved algorithm is simple and effective to solve the small-scale knapsack problem.
Keywords :
convergence; knapsack problems; particle swarm optimisation; individual particle position; load-bearing quantity; low convergence accuracy; particle swarm optimization algorithm; population position; small-scale knapsack problem; standard PSO algorithm; Algorithm design and analysis; Genetic algorithms; Mathematical model; Optimization; Particle swarm optimization; Search problems; Standards; GA; PSO; knapsack problem; small-scale;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
ISSN :
2157-9555
Print_ISBN :
978-1-4577-2130-4
Type :
conf
DOI :
10.1109/ICNC.2012.6234615
Filename :
6234615
Link To Document :
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