Improved particle swarm optimization algorithm based on statistical laws and dynamic learning factors.

*(English)*Zbl 1157.68414
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 210-214 (2008).

Summary: Particle swarm optimization (PSO) algorithm has the disadvantage that, once it gets into the local optimization it is very hard to jump out from the local optimization. For that reason, a novel improved Particle Swarm Optimization algorithm is presented in this paper. The algorithm can use statistical laws of particle fitting value to classify the particles, and take different evolution models for different kinds of particles. And for the particles evolved in full model, learning factor is adjusted dynamically, which can enhance the evolution efficiency and precision greatly. By the experiments and analysis, the optimization variation rule which evolved with the learning factor is achieved, and the function expressions of learning factor \(C_1\) and \(C_2\) are given in this paper. The simulation results showed that, compared with other PSO algorithms proposed before, it is improved virtually on both optimization precision and optimization efficiency by using the improved PSO algorithm to optimize four typical benchmark functions.

For the entire collection see [Zbl 1154.68012].

For the entire collection see [Zbl 1154.68012].

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\textit{X.-j. Bi} et al., in: Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press. 210--214 (2008; Zbl 1157.68414)