基于莱维飞行的改进粒子群算法

doi:10.16182/j.issn1004731x.joss.201708007

摘要/Abstract

摘要： 针对基本粒子群算法容易发生早熟收敛,陷入局部极值,进化后期收敛速度慢以及精度低等缺点,提出了基于莱维飞行的改进粒子群算法。在粒子位置更新公式中,消除速度项对收敛速度的影响,利用莱维飞行改变粒子位置移动方向,防止粒子陷入局部最优值,通过贪婪的更新评价策略,选择最优解,从而得到全局最优。实验结果表明,与基本粒子群算法、布谷鸟搜索算法以及蜂群算法相比,所提出的基于莱维飞行的改进粒子群算法能够有效地提高解的精度并加快收敛速度,寻优效果更优。

关键词: 粒子群算法, 莱维飞行, 贪婪策略, 优化

Abstract: The particle swarm optimization (PSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The Lévy particle swarm optimization (Lévy PSO) was proposed. In the particle position updating formula, Lévy PSO eliminated the impact of speed on the convergence rate, and used Levy flight to change the direction of particle positions movement to prevent particles getting into local optimum value, and then using greedy strategy to update the evaluation and choose the best solution to obtain the global optimum. The experimental results show that Lévy PSO can effectively improve the accuracy and convergence speed and the Lévy PSO has better optimization effect than PSO, Cuckoo Search (CS) and Artificial Bee Colony Algorithm (ABC).

Key words: particle swarm optimization, Lévy fights;, greedy strategy, optimization

中图分类号:

TP301.6

李荣雨, 王颖. 基于莱维飞行的改进粒子群算法[J]. 系统仿真学报, 2017, 29(8): 1685-1692.

Li Rongyu, Wang Ying. Improved Particle Swarm Optimization Based on Lévy Flights[J]. Journal of System Simulation, 2017, 29(8): 1685-1692.

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