摘要
为提升粒子群优化算法的性能,文章提出了一种融合改进莱维飞行策略的混沌粒子群算法chaoLPSO。首先,通过引入改进的混沌序列来初始化粒子群,从而增强了算法的全局搜索能力;同时,采用分段式自适应权重策略来调整粒子的速度和位置,以提高算法的收敛速度;此外,通过引入改进的莱维飞行策略,增加了搜索范围,有助于克服局部最优问题。在4个经典测试函数上的验证结果表明,chaoLPSO算法在搜索性能和收敛速度方面均优于传统粒子群算法。
To enhance the performance of the particle swarm optimization algorithm,the paper proposes a chaotic particle swarm algorithm,chaoLPSO,which integrates an improved Lévy flight strategy.Firstly,an improved chaotic sequence is introduced to initialize the particle swarm,thereby enhancing the algorithm’s global search capability.At the same time,a segmented adaptive weight strategy is used to adjust the velocity and position of the particles to improve the algorithm’s convergence speed.Additionally,by incorporating an improved Lévy flight strategy,the search range is expanded,which helps to overcome the local optimum problem.Validation results on four classic test functions show that the chaoLPSO algorithm outperforms the traditional particle swarm algorithm in terms of search performance and convergence speed.
作者
段艳
潘峰
DUAN Yan;PAN Feng(School of Data Science and Information Engineering,Guizhou Minzu University,Guiyang 550025,China)
出处
《长江信息通信》
2025年第1期67-69,72,共4页
Changjiang Information & Communications
基金
贵州省教育厅自然科学研究项目,贵州省高等学校大数据分析与智能计算重点实验室(编号:黔教技[2023]012号)
贵州省高等学校智能算法与智能软件协同创新团队(编号:黔教技[2023]061号)
贵州省高等学校光通讯系统中孤子的数学理论与计算协作创新团队(编号:黔教技[2023]062号)。
关键词
混沌优化
粒子群算法
莱维飞行
全局搜索
优化问题
chaotic optimization
particle swarm algorithm
levy flight
global search
optimization problem
