Abstract
This paper presents a new hybrid algorithm of particle swarm optimization (PSO) called PSOSA, in which the mechanism of modified simulated annealing (SA) is embedded into standard PSO algorithm. The proposed algorithm not only keeps the characters of simple and easy to be implemented, but also enhances the ability of getting rid of local optimum and improves the speed and precision of convergence. The testing results of several benchmark functions with different dimensions show that the proposed algorithm is superior to standard PSO and the other PSO algorithms.
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Yang, G., Chen, D., Zhou, G. (2006). A New Hybrid Algorithm of Particle Swarm Optimization. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_6
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DOI: https://doi.org/10.1007/11816102_6
Publisher Name: Springer, Berlin, Heidelberg
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