一种改进的粒子群算法

Wen Shuhua, Zhang Xueliang, Liu Hainan, Liu Shuyang, Wang Jiaying
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引用次数: 161

摘要

提出了一种基于种群适应度方差的改进粒子群优化算法。在计算过程中,根据种群适应度的方差自适应随机确定MPSO的惯性权值。并且大大提高了粒子群优化算法(PSO)摆脱局部最优的能力。仿真结果表明,该算法与标准的简单粒子群算法相比,不仅具有较强的收敛性,而且能有效地避免早熟收敛问题
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A Modified Particle Swarm Optimization Algorithm
A modified particle swarm optimization (MPSO) algorithm is presented based on the variance of the population's fitness. During computing, the inertia weight of MPSO is determined adaptively and randomly according to the variance of the populations fitness. And the ability of , particle swarm optimization algorithm (PSO) to break away from the local optimum is greatly improved. The simulating results show that this algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively
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