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Information Technology Journal

Year: 2009 | Volume: 8 | Issue: 8 | Page No.: 1263-1268
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Authors

Xue-yao Gao

Li-quan Sun

Da-song Sun

Keywords

Research Article

An Enhanced Particle Swarm Optimization Algorithm

Xue-yao Gao, Li-quan Sun and Da-song Sun
Particle Swarm Optimization (PSO) algorithm is often used for finding optimal solution, but it easily entraps into the local extremum in later evolution period. Based on improved chaos searching strategy, an enhanced particle swarm optimization algorithm is proposed in this study. When particles get into the local extremum, they are activated by chaos search strategy, where the chaos search area is controlled in the neighborhood of current optimal solution by reducing search area of variables. The new algorithm not only gets rid of the local extremum effectively but also enhances the precision of convergence significantly. Experiment results show that the proposed algorithm is better than standard PSO algorithm in both precision and stability.
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