Gaussion Mutation Particle Swarm Optimization with Dynamic Adaptation Inertia Weight

Lili Li, Xingshi He
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引用次数: 5

Abstract

An improved PSO with decreasing inertia weight is proposed in this paper, which is different from the inertia weight of standard PSO. In addition, a new social component instead of the old one to make more explore and a tiny Gauss perturbation joined in the position equation to help maintain swarm diversity. Four standard test functions with asymmetric initial range settings are used to prove its validity. Experimental results verify its superiority both in convergent speed and solution precision. Conclusions are drawn in the end
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动态自适应惯性权的高斯突变粒子群优化
本文提出了一种改进的粒子群算法,该算法与标准粒子群算法的惯性权重不同,其惯性权重减小。此外,一个新的社会成分代替旧的,使更多的探索和一个微小的高斯扰动加入到位置方程,以帮助保持群体的多样性。采用非对称初始量程设置的四个标准测试函数验证了该方法的有效性。实验结果验证了该方法在收敛速度和求解精度上的优越性。最后得出结论
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