Abstract
Particle swarm optimization (PSO) algorithm is an intelligent search method based on swarm intelligence. It has been widely used in many fields because of its conciseness and easy implementation. But it is also easy to be plunged into local solution and its later convergence speed is very slow. In order to increase its convergence speed, nonlinear simplex method (NSM) is integrated into it, which not only can increase its later convergence speed but also can effectively avoid dependence on initial conditions of NSM. In order to bring particles jump out of local solution regions, tabu search (TS) algorithm is integrated into it to assign tabu attribute to these regions, which make it with global search ability. Thus the hybrid PSO algorithm is an organic composition of the PSO, NSM and TS algorithms. Finally its basic operation process and optimization characteristics are analyzed through some benchmark functions and its effectiveness is also verified.
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Li, Z., Zheng, D., Hou, H. (2010). A Hybrid Particle Swarm Optimization Algorithm Based on Nonlinear Simplex Method and Tabu Search. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_17
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DOI: https://doi.org/10.1007/978-3-642-13278-0_17
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