Abstract
This paper talks about the problems in particle swarm optimization (PSO), including local optimum and difficulty in improving solution accuracy by fine tuning. We presents a new variation of Adaptive Tribe-PSO model where nonlinear updating of inertia weight and a particle's fitness with Tribe-PSO model are combined to improve the speed of convergence as well as fine tune the search in the multidimensional space. The method proved to be a powerful global optimization algorithm. © 2011 Springer-Verlag.
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CITATION STYLE
Song, Y. D., Zhang, L., & Han, P. (2011). An adaptive tribe-particle swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6728 LNCS, pp. 86–92). https://doi.org/10.1007/978-3-642-21515-5_11
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