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\iteman{io-port 06091713}
\itemau{Zhao, Chengye; Yan, Zhengbing; Liu, Xinggao}
\itemti{An improved adaptive parameter particle swarm optimization algorithm.}
\itemso{J. Zhejiang Univ., Eng. Sci. 45, No. 12, 2099-2102 (2011).}
\itemab
Summary: A modified adaptive parameter particle swarm optimization (MAPSO) algorithm is proposed to enhance the convergence speed and optimization accuracy of the standard particle swarm optimization (PSO) algorithm. The algorithm is based on evolutionary factor calculation and evolutionary status estimate, and introduces parameter control and mutation operator to improve the convergence speed and the global optimization ability. Benchmark test comparison among two existing algorithms and the MAPSO algorithm is carried out. The experimental results show that the MAPSO algorithm can get better solution on 4 in 6 benchmark test functions and can converge faster on 5 in 6 benchmark test functions than the other two algorithms. The results reveal the advantage of the proposed algorithm in different optimization indexes, which is supposed to have a promising potential in solving the optimization problems of the practical process engineering.
\itemrv{~}
\itemcc{}
\itemut{adaptive particle swarm optimization algorithm; parameter control; evolutionary status estimate}
\itemli{doi:10.3785/j.issn.1008-973X.2011.12.005}
\end