Abstract
In order to improve performance of particle swarm optimization algorithm (PSO) in global optimization, the reason of premature convergence of the PSO is analyzed, and a new particle swarm optimization based on two sub-swarms (TSS-PSO) is proposed in this paper. The particle swarm is divided into two identical sub-swarms, that is, the first sub-swarm adopts basic PSO model to evolve, whereas the second sub-swarm iterates adopts the cognition only model. In order to enhance the diversity and improve the convergence of the PSO, the worst fitness of the first sub-swarm is exchanged with the best fitness of the second sub-swarm in each iterate for increasing the information exchange between the particles. Compared with other two sub-swarms algorithms, the idea of this algorithm is readily comprehended, and its program is easy to be realized. The experimental results display that the convergence of TSS-PSO evidently gets the advantage of basic particle swarm optimization, as well as its competence of finding the global optimal solution is better than the basic PSO.
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Yu, Z., Wu, W., Wu, L. (2012). An Improved Particle Swarm Optimization Algorithm Based on Two Sub-swarms. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30223-7_69
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DOI: https://doi.org/10.1007/978-3-642-30223-7_69
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