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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 622-625
ABSTRACT
In order to avoid premature convergence to local minimum, an improved particle swarm optimization (PSO) algorithm is proposed in this paper. The proposed approach adaptively adjusts its inertia weight according to the change of population fitness, and executes its mutation operation in accordance with its population density. The algorithm's performance is tested through three typical test function experiments. The test results and analysis show that it obviously enhances the performance and improves the population density.
CITATION
Mo Lin, Zheng Hua, "Improved PSO Algorithm with Adaptive Inertia Weight and Mutation", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 622-625, doi:10.1109/CSIE.2009.428
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