具有拓扑时变和搜索扰动的混合粒子群优化算法  

Hybrid particle swarm optimization algorithm with topological time-varying and search disturbance

作　　者：周文峰 梁晓磊 唐可心 李章洪 符修文 ZHOU Wenfeng;LIANG Xiaolei;TANG Kexin;LI Zhanghong;FU Xiuwen(School of Automobile and Traffic Engineering,Wuhan University of Science and Technology,Wuhan Hubei 430065,China;Institute of Logistics Science and Engineering,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]武汉科技大学汽车与交通工程学院,武汉430065 [2]上海海事大学物流科学与工程研究院,上海201306

出　　处：《计算机应用》2020年第7期1913-1918,共6页journal of Computer Applications

基　　金：国家自然科学基金青年基金资助项目(61603280,61902238)。

摘　　要：粒子群优化(PSO)算法在求解复杂多峰函数时极易早熟,陷入局部最优无法跳出。研究表明改变粒子间的拓扑结构和调整算法的迭代机制有助于改善种群的多样性,提高算法的寻优能力。因此,提出一种具有拓扑时变和搜索扰动的混合粒子群优化(HPSO-TS)算法。该算法采用K-medoids聚类算法对粒子群进行动态分簇,形成多个异构子群,以利于子群内粒子间进行信息流通。在速度更新中,增加簇最优粒子的引导,并引入非线性变化极值扰动,帮助粒子搜索更多的区域。而后在位置迭代中引入花授粉算法(FPA)中的转换概率,使粒子在全局搜索和局部搜索之间转换。在全局搜索时结合狮群算法中的母狮觅食机制对粒子的位置进行更新;在局部搜索时引入正弦扰动因子,帮助粒子跳出局部最优。实验结果表明所提算法在求解精度和鲁棒性方面明显优于FPA、PSO、改进粒子群算法(IPSO)、具有动态拓扑结构的粒子群算法(PSO-T);并且随着测试维度和次数的增加,这种优势更加明显。HPSO-TS算法所引入的拓扑时变策略和搜索扰动机制能有效地提高种群的多样性和粒子的活性,从而改善寻优能力。Particle Swarm Optimization(PSO)algorithm is easy to be premature and drop into the local optimum and cannot jump out when solving complex multimodal functions.Related researches show that changing the topological structure among particles and adjusting the updating mechanism are helpful to improve the diversity of the population and the optimization ability of the algorithm.Therefore,a Hybrid PSO with Topological time-varying and Search disturbance(HPSOTS)was proposed.In the algorithm,a K-medoids clustering algorithm was adapted to cluster the particle swarm dynamically for forming several heterogeneous subgroups,so as to facilitate the information flow among the particles in the subgroups.In the speed updating,by adding the guide of the optimal particle of the swarm and introducing the disturbance of nonlinear changing extreme,the particles were able to search more areas.Then,the transformation probability of the Flower Pollination Algorithm(FPA)was introduced into the position updating process,so the particles were able to transform their states between the global search and the local search.In the global search,a lioness foraging mechanism in the lion swarm optimization algorithm was introduced to update the positions of the particles;while in the local search,a sinusoidal disturbance factor was applied to help particles jump out of the local optimum.The experimental results show that the proposed algorithm is superior to FPA,PSO,Improved PSO(IPSO)algorithm and PSO algorithm with Topology(PSO-T)in the accuracy and robustness.With the increase of testing dimension and times,these advantages are more and more obvious.The topological time-varying strategy and search disturbance mechanism introduced by this algorithm can effectively improve the diversity of population and the activity of particles,so as to improve the optimization ability.

关 键 词：粒子群优化算法 拓扑时变 搜索扰动 聚类 极值扰动 转换概率 正弦扰动因子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]