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论文:2023,Vol:41,Issue(2):428-438 |
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引用本文： |
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黄懿, 梁放驰, 范成礼, 宋占福. 带随机变异及感知因子的粒子群优化算法[J]. 西北工业大学学报 |
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HUANG Yi, LIANG Fangchi, FAN Chengli, SONG Zhanfu. Improved particle swarm optimization algorithm with random mutation and perception[J]. Journal of Northwestern Polytechnical University |
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| 带随机变异及感知因子的粒子群优化算法 |
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| 黄懿1, 梁放驰1, 范成礼2, 宋占福2 |
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1. 空军工程大学 基础部, 陕西 西安 710051;
2. 空军工程大学 防空反导学院, 陕西 西安 710051 |
| 摘要: |
| 针对传统粒子群算法(PSO)在求解高维空间中复杂函数时容易发生"早熟"现象,根据粒子在空间中的运动规律和散布特点,提出带随机变异因子和动态感知因子的粒子群优化算法。算法通过引入对邻域具有质疑策略的随机变异因子,促使个体粒子对自身邻域进行探索,降低粒子因过于信赖个体最优和全局最优而发生的"早熟"现象,从而改进速度更新策略;同时,为粒子位置更新引入感知因子,使粒子在同一维度上动态自适应控制自身与其他粒子的空间距离,从而避免陷入局部最优。通过测试函数实验、算法对比分析实验、随机参数影响实验和算法复杂性实验,验证了该算法在求解高维空间中的复杂函数等问题时,具有明显的优越性和鲁棒性。 |
| 关键词:
粒子群优化算法
随机变异因子
动态感知因子
局部最优
全局最优
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| Improved particle swarm optimization algorithm with random mutation and perception |
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| HUANG Yi1, LIANG Fangchi1, FAN Chengli2, SONG Zhanfu2 |
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1. Fundamentals Department, Air Force Engineering University, Xi'an 710051, China;
2. School of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China |
| Abstract: |
| Since traditional particle swarm optimization(PSO) is prone to premature phenomenon when solving complex functions in high-dimensional space, a particle swarm optimization algorithm with random variation and dynamic perception factors in terms of the movement laws and dispersion characteristics of particles in space is proposed. In order to encourage individual particles to explore their own neighborhoods and reduce the premature phenomenon of particles due to over-reliance on individual optimality and global optimality, a random mutation factor with a questioning strategy for neighborhoods is added to the basic algorithm to improve the speed update. At the same time, a perception factor is added to the particle position update, so that the particle can dynamically and adaptively control the spatial distance between itself and other particles in the same dimension, so as to avoid falling into local optimum. The algorithm has obvious superiority and robustness in solving complex functions in high-dimensional space through test function experiments, algorithm comparison analysis experiments, random parameter influence experiments and algorithm complexity experiments. |
| Key words:
particle swarm optimization algorithm
random variation factor
dynamic perception factor
local optimum
global optimum
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| 收稿日期: 2022-06-22
修回日期:
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| DOI: 10.1051/jnwpu/20234120428 |
| 基金项目: 国家自然科学基金(72001214)资助 |
| 通讯作者: 宋占福(1992-),空军工程大学博士研究生,主要从事防空反导作战运筹研究。e-mail:2011songfzy@sina.com
Email：2011songfzy@sina.com |
| 作者简介: 黄懿(1993-),空军工程大学讲师,主要从事最优化理论与算法研究。
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