Abstract
Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. In order to get rid of the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, this paper improves the standard PSO and proposes a new algorithm to solve these problems. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Compared with standard PSO on the Benchmarks function, the new algorithm produces more efficient results. © Springer-Verlag 2009.
Cite
CITATION STYLE
Yan, X., Wu, Q., & Liu, H. (2009). A new optimizaiton algorithm for function optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5821 LNCS, pp. 144–150). https://doi.org/10.1007/978-3-642-04843-2_16
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