|Table of Contents|

Multi-strategy adaptive particle swarm optimization algorithm(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年03期
Page:
301-
Research Field:
Publishing date:

Info

Title:
Multi-strategy adaptive particle swarm optimization algorithm
Author(s):
Tang Kezong1Feng Jianwen1Li Fang1Yang Jingyu2
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 2.Key Laboratory of Intelligent Perception and Systems for High-dimensional Information ofMinistry of Education,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
particle swarm optimization diversity-measurement real-time alternating strategy elitist learning strategy population diversity
PACS:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2017.41.03.005
Abstract:
In order to improve the efficiency of particle swarm optimization(PSO)algorithm for searching for optimal solutions,a multi-strategy adaptive particle swarm optimization(MAPSO)algorithm is proposed.A diversity-measurement strategy is developed to evaluate the population distribution.A real-time alternating strategy is performed to determine predefined evolutionary states,exploration or exploitation.During iterative optimization,the inertia weight is dynamically controlled according to the diversity of particles.An elitist learning strategy is introduced to enhance population diversity and to prevent the population from possibly falling into local optimal solutions.Experimental results show that,compared with the adaptive particle swarm optimization(APSO),comprehensive learning particle swarm optimization(CLPSO)and perturbed particle swarm optimization(PPSO),the MAPSO can substantially enhance the ability of jumping out of the local optimal solutions and significantly improve the search efficiency and convergence speed.

References:

[1] Kennedy J,Eberhart R C.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Perth,Australia:IEEE,1995:1942-1948.
[2]Tang Kezong,Wu Jun.Improved multi-objective differential evolution for maintaining population diversity[J].International Conference on Natural Computation,2013,7(3):522-527.
[3]杨宁,霍炬,杨明.基于多层次信息交互的多目标粒子群优化算法[J].控制与决策,2016,31(5):907-912.
Yang Ning,Huo Ju,Yang Ming.Multi-objective particle swarm optimization algorithm based on the interaction of multi-level information[J].Control and Decision,2016,31(5):907-912.
[4]Zhao Xinchao.A perturbed particle swarm algorithm for numerical optimization[J].Applied Soft Computing,2010,10(1):119-124.
[5]Zhan Zhihui,Zhang Jun,Li Yun,et al.Adaptive particle swarm optimization[J].IEEE Transactions on Systems,Man,and Cybernetics—Part B(Cybernetics),2009,39(6):1362-1381.
[6]Du Weilin,Li Bin.Multi-strategy ensemble particle swarm optimization for dynamic optimization[J].Information Sciences,2008,178(15):3096-3109.
[7]汤可宗,李慧颖,李娟,等.一种求解复杂优化问题的改进粒子群优化算法[J].南京理工大学学报,2015,34(4):386-391.
Tang Kezong,Li Huiying,Li Juan,et al.Improved particle swarm optimization algorithm for solving complex optimization problems[J].Journal of Nanjing University of Science and Technology,2015,34(4):386-391.
[8]Yu Huanjun,Zhang Liping,Chen Dezhao,et al.Adaptive particle swarm optimization algorithm based on feedback mechanism[J].Chinese Journal of Zhejiang Univerisity,2005,39(9):1286-1291.
[9]Riget J,Vesterstr?m J S.A diversity-guided particle swarm optimizer—the ARPSO[R].Aarhus,Denmark:University of Aarhus,2002.
[10]Liang J J,Qin A K,Suganthan P N,et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J].IEEE Transactions on Evolutionary Computation,2006,10(3):281-295.

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Last Update: 2017-06-30