|
|
|
| Dynamic Probabilistic Particle Swarm Optimization Based on Heterogeneous Multiple Population Strategy |
| NI Qing-Jian1,2, DENG Jian-Ming1, XING Han-Cheng1 |
1.School of Computer Science and Engineering, Southeast University, Nanjing 211189
2.Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006 |
|
|
|
|
Abstract Aiming at premature convergence and the slow search speed of the traditional particle swarm optimization, a heterogeneous multiple population strategy is combined with the characteristics of dynamic probabilistic particle swarm optimization (DPPSO). In the evolutionary process of DPPSO with the strategy, multiple sub-populations are maintained and each sub-population evolves with different DPPSO variants. According to certain rules, communication between the sub-populations are executed to maintain the information exchange inside the entire population and coordinate exploration and exploitation. DPPSO algorithms with the strategy are tested on four benchmark functions which are commonly used in the evolutionary computation. Experimental results demonstrate that the DPPSO with the strategy significantly improves the convergence speed and stability with strong global search ability.
|
|
Received: 13 May 2013
|
|
|
|
|
|
[1] Esmin A A A, Coelho R A, Matwin S. A Review on Particle Swarm Optimization Algorithm and Its Variants to Clustering High-Dimensional Data. Artificial Intelligence Review, 2013. DOI: 10.1007/s10462-013-9400-4
[2] AlRashidi M R, El-Hawary M E. A Survey of Particle Swarm Optimization Applications in Electric Power Systems. IEEE Trans on Evolutionary Computation, 2009, 13(4): 913-918
[3] Kulkarni R V, Venayagamoorthy G K. Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey. IEEE Trans on Systems, Man and Cybernetics, 2011, 41(2): 262-267
[4] Shi Y H. Particle Swarm Optimization. IEEE Connections, 2004, 2(1): 8-13
[5] Zhao S Z, Suganthan P N, Pan Q K, et al. Dynamic Multi-swarm Particle Swarm Optimizer with Harmony Search. Expert Systems with Applications: An International Journal, 2011, 38(4): 3735-3742
[6] Liu Y M, Sui C L, Zhao Q Z. Dynamic Multi-swarm Particle Swarm Optimizer Based on K-means Clustering and Its Application. Control and Decision, 2011, 26(7): 1019-1025 (in Chinese)
(刘衍民,隋常玲,赵庆祯.基于K-均值聚类的动态多种群粒子群算法及其应用.控制与决策, 2011, 26(7): 1019-1025)
[7] Marinakis Y, Marinaki M. A Hybrid Multi-swarm Particle Swarm Optimization Algorithm for the Probabilistic Traveling Salesman Problem. Computers & Operations Research, 2010, 37(3): 432-442
[8] Guo Y N, Cheng J, Cao Y Y, et al. Multi-population Particle Swarm Cultural Algorithms Adopting Chaotic Knowledge Migration. Control Theory & Applications, 2011, 28(9): 1056-1062 (in Chinese)
(郭一楠,程 健,曹媛媛,等.基于混沌知识迁移的多种群粒子群文化算法.控制理论与应用, 2011, 28(9): 1056-1062)
[9] Liang J J, Qu B Y, Suganthan P N, et al. Dynamic Multi-swarm Particle Swarm Optimization for Multi-objective Optimization Pro-blems // Proc of the IEEE Congress on Evolutionary Computation. Brisbane, Australia, 2012: 1-8
[10] Kennedy J. Dynamic-Probabilistic Particle Swarms // Proc of the Conference on Genetic and Evolutionary Computation. Washington, USA, 2005: 201-207
[11] Ni Q J, Xing H C, Zhang Z Z, et al. Experiment and Analysis on Dynamic Probabilistic Particle Swarm Optimization Model. Computer Science, 2009, 36(2): 222-226 (in Chinese)
(倪庆剑,邢汉承,张志政,等.动态概率粒子群优化模型及实验分析.计算机科学, 2009, 36(2): 222-226)
[12] Ni Q J, Zhang Z Z, Wang Z Z, et al. Dynamic Probabilistic Particle Swarm Optimization Based on Varying Multi-cluster Structure. Journal of Software, 2009, 20(2): 339-349 (in Chinese)
(倪庆剑,张志政,王蓁蓁,等.一种基于可变多簇结构的动态概率粒子群优化算法.软件学报, 2009, 20(2): 339-349)
[13] Ni Q J, Deng J M. Two Improvement Strategies for Logistic Dynamic Particle Swarm Optimization // Proc of the 10th International Conference on Adaptive and Natural Computing Algorithms. Ljubljana, Slovenia, 2011, I: 320-329 |
|
|
|
2014, Vol. 27 
