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A New Adaptive Particle Swarm Optimization Algorithm

机译:一种新的自适应粒子群优化算法

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摘要

A new adaptive particle swarm optimization algorithm is proposed in this paper. Every particle chooses its inertial factor according to the fitness of itself and the optimal particle in the presented algorithm. Simulation results show that the new algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence. At the same time, the experimental results also show that the suggested algorithm is greatly superior to PSO and APSO in terms of robustness.
机译:本文提出了一种新的自适应粒子群优化算法。每个颗粒根据本身的适应性和所呈现的算法中的最佳粒子选择其惯性因素。仿真结果表明,新算法具有全球收敛性的优势,可有效缓解过早收敛的问题。同时,实验结果还表明,在鲁棒性方面,建议的算法大大优于PSO和APSO。

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