Improved PSO Algorithm with Adaptive Inertia Weight and Mutation
Mo Lin, Hua Jiang Zheng · 2009
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.