A New Class of Hybrid Particle Swarm Optimization Algorithm
作者： Da-Qing Guo , Xiao Li , Yong-Jin Zhao , Hui Xiong
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摘要: A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, linearly decreasing inertia weight technique (LDIW) and mutative scale chaos (MSCOA) are combined with PSO, which used to balance global local exploration abilities enhance searching abilities, respectively. order evaluate performance method, three benchmark functions used. The simulation results confirm proposed can greatly ability effectively improve convergence.
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