《计算机应用研究》|Application Research of Computers

一种基于动态惯性权重的鸟群优化算法

Bird swarm algorithm based on dynamic inertia weight

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作者 高宏进,王力
机构 贵州大学 大数据与信息工程学院,贵阳 550025
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文章编号 1001-3695(2019)05-020-1376-04
DOI 10.19734/j.issn.1001-3695.2017.11.0784
摘要 鸟群算法(BSA)作为一种新型的元启发式群智能算法,存在易陷入局部最优、收敛速度慢和求解精度低等问题。针对原鸟群算法在求解最优化问题中的不足,提出一种基于动态惯性权重的鸟群优化算法(DBSA)。该算法通过引入非线性动态惯性权重修正鸟群飞行间隔,平衡种群全局搜索与局部搜索能力;在模拟鸟群生产者觅食的过程中引入莱维飞行,替换原算法中生产者的觅食策略提高算法活力和有效性。实验表明改进后的鸟群算法有效提高了算法的收敛速度和寻优精度。
关键词 鸟群算法; 函数优化; 动态惯性权重; 莱维飞行
基金项目
本文URL http://www.arocmag.com/article/01-2019-05-020.html
英文标题 Bird swarm algorithm based on dynamic inertia weight
作者英文名 Gao Hongjin, Wang Li
机构英文名 School of Big Date & Information Engineering Guizhou University,Guiyang 550025,China
英文摘要 As a new kind of heuristic swarm intelligence algorithm, the bird swarm algorithm(BSA) easily falls into problems about local optimal, slow convergence speed and low resolution accuracy. Considering the fact that the original bird swarm algorithm is not sufficient to solve the issue in terms of optimization, this paper proposed an optimization algorithm, dynamic inertia weight-bird swarm algorithm(DBSA). The algorithm corrected birds flying interval by introducing nonlinear dynamic inertia weight, balancing the abilities of population global search and local search; this paper introduced the parameter of levy flight in the process of simulation of the foraging birds producer, advancing algorithm's vitality and effectiveness via replacing the original algorithm producers foraging strategies. As a result, experiments show that the modified algorithm improves the convergence speed and optimization accuracy effectively.
英文关键词 birds swarm algorithm; function optimization; dynamic inertia weight; Levy flight
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收稿日期 2017/11/23
修回日期 2018/2/5
页码 1376-1379,1384
中图分类号 TP391.41;TP301.6
文献标志码 A