融合密度峰值决策的粒子群优化算法

doi:10.16088/j.issn.1001-6600.2025040101

广西师范大学学报（自然科学版） ›› 2026, Vol. 44 ›› Issue (2): 145-163.doi: 10.16088/j.issn.1001-6600.2025040101

融合密度峰值决策的粒子群优化算法

赵晨颖¹, 袁书娟^1*, 孔闪闪¹, 杨爱民^1,2,3,4,5, 魏佳妹¹

1.华北理工大学理学院,河北唐山 063210;
2.铁矿石优选与铁前工艺智能化河北省工程研究中心(华北理工大学),河北唐山 063210;
3.河北省数据科学与应用重点实验室(华北理工大学),河北唐山 063210;
4.唐山市工程计算重点实验室(华北理工大学),河北唐山 063210;
5.唐山市智能工业与图像处理技术创新中心(华北理工大学),河北唐山 063210

收稿日期:2025-04-01 修回日期:2025-07-05 发布日期:2026-02-03
通讯作者: 袁书娟(1980—),女,河北迁安人,华北理工大学副教授。E-mail: yuanshujuan@ncst.edu.cn
基金资助:
国家自然科学基金(52074126);河北省自然科学基金(E2022209110)

Particle Swarm Optimization Algorithm with Density PeakClustering Decision Values

ZHAO Chenying¹, YUAN Shujuan^1*, KONG Shanshan¹, YANG Aimin^1,2,3,4,5, WEI Jiamei¹

1. College of Science, North China University of Science and Technology, Tangshan Hebei 063210, China;
2. Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Mate rialsPreparation Processes (North China University of Science and Technology), Tangshan Hebei 063210, China;
3. Hebei Key Laboratory of Data Science and Application (North China University of Science and Technology), Tangshan Hebei 063210, China;
4. The Key Laboratory of Engineering Computing in Tangshan City (North China Universityof Science and Technology), Tangshan Hebei 063210, China;
5. Tangshan Intelligent Industry and Image ProcessingTechnology Innovation Center (North China University of Science and Technology), Tangshan Hebei 063210, China

Received:2025-04-01 Revised:2025-07-05 Published:2026-02-03

摘要/Abstract

摘要： 粒子群优化算法(PSO)作为群智能优化的一种经典算法得到广泛应用,但其面对不同问题时不能根据群体状态进行实时调整,缺乏一定灵活性。为此,本文提出一种融合密度峰值决策的粒子群优化算法(DVPSO)。针对初始化,设计精英佳点集双型映射,提升不同类型粒子分布质量;针对速度更新,构建基于密度峰值的信息交互机制,平衡粒子搜索倾向;针对位置更新,提出步长搜索算子的动态双邻域搜索策略,结合种群状态及寻优范围变化,调控粒子移动的同时兼顾搜索灵活性。仿真实验选用12个测试函数,将DVPSO与PSO及其他5种较新的群智能优化算法进行对比,并在2个工程问题上与5种新兴智能算法对比。结果表明,DVPSO算法具备较好的搜索精度和稳定性,验证了算法的适应性和良好性能。

关键词: 粒子群优化算法, 密度峰值, 精英佳点集, 信息交互, 动态邻域搜索

Abstract: Particle swarm optimization (PSO) algorithm, as a classical algorithm of swarm intelligence optimization, has been widely used in practice. However, in the face of different problems, it cannot make real-time adjustments according to the group status, and lacks certain flexibility, therefore, a particle swarm optimization algorithm (DVPSO) based on fusion density peak decision is proposed. For initialization, design the binary mapping of elite point set in order to improve the distribution quality of different types of particles. For velocity update, an information exchange mechanism based on density peak is constructed to balance the particle search tendency. For position updating, a dynamic two-neighborhood search strategy of step size search operator is proposed, which combines population state and optimization range changes to regulate particle movement and give consideration to search flexibility. 12 test functions were compared with PSO and 5 new swarm intelligent optimization algorithms, and 2 engineering problems were compared with 5 new intelligent algorithms. The results show that the DVPSO algorithm has better search accuracy and stability, which verifies the adaptability and good performance of the algorithm.

Key words: particle swarm optimization algorithm, peak density, elite point set, information interaction, dynamic neighborhood search

中图分类号: TP301.6;TP391;TP18

赵晨颖, 袁书娟, 孔闪闪, 杨爱民, 魏佳妹. 融合密度峰值决策的粒子群优化算法[J]. 广西师范大学学报（自然科学版）, 2026, 44(2): 145-163.

ZHAO Chenying, YUAN Shujuan, KONG Shanshan, YANG Aimin, WEI Jiamei. Particle Swarm Optimization Algorithm with Density PeakClustering Decision Values[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(2): 145-163.

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融合密度峰值决策的粒子群优化算法

Particle Swarm Optimization Algorithm with Density PeakClustering Decision Values

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