International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.1, No.1, Oct. 2009
A Novel Particle Swarm Optimization Algorithm Model with Centroid and its Application
Full Text (PDF, 111KB), PP.42-49
In order to enhance inter-particle cooperation and information sharing capabilities, an improved particle swarm algorithm optimization model is proposed by introducing the centroid of particle swarm in the standard PSO model to improve global optimum efficiency and accuracy of algorithm, then parameter selection guidelines are derived in the convergence of new algorithm. The results of Benchmark function simulation and the material balance computation (MBC) in alumina production show the new algorithm, with both a steady convergence and a better stability, not only enhance the local searching efficiency and global searching performance greatly, but also have faster higher precision and convergence speed, and can avoid the premature convergence problem effectively.
Cite This Paper
Shengli Song, Li Kong, Jingjing Cheng,"A Novel Particle Swarm Optimization Algorithm Model with Centroid and its Application Shengli Song", International Journal of Intelligent Systems and Applications(IJISA), vol.1, no.1, pp.42-49, 2009. DOI: 10.5815/ijisa.2009.01.05
J. Kennedy, R. C. Eberhart. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks IV, IEEE Press, Piscataway, NJ (1995),pp.1942–1948.
R. C. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, IEEE Press, Piscataway, NJ (1995),pp.39–43.
Seo Jang-Ho, Heo Chang-Geun, Kim Jae-Kwang et al. Multimodal function optimization based on particle swarm optimization. IEEE Transactions on Magnetics, v42, n4, April, 2006, pp.1095-1098.
Iwamatsu Masao. Multi-species particle swarm optimizer for multimodal function optimization. IEICE Transactions on Information and Systems, v E89-D, n3, 2006, pp.1181-1187.
Korenaga Takeshi, Hatanaka Toshiharu, Uosaki Katsuji. Performance improvement of particle swarm optimization for high-dimensional function optimization. 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2008, pp.3288-3293.
G. K. Venayagamoorthy, S. Doctor. Navigation of Mobile Sensors Using PSO and Embedded SO in a Fuzzy Logic Controller. IEEE 39th Industry Applications Conference. 2004, pp.1200-1206.
Yufei Zhang, Zhiyan Dang, Jie Wei. Research and simulation of fuzzy controller design based on particle swarm optimization. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), v1, 2006, pp.3757-3761.
M. A. El-Telbany, H.G. Konsowa, M. El-Adawy. Studying the predictability of neural network trained by particle swarm optimization. Journal of Engineering and Applied Science, v53, n3, June, 2006,pp. 377-390.
Su Rijian, Kong Li, Song Shengli et al. A new ridgelet neural network training algorithm based on improved particle swarm optimization. Proceedings - Third International Conference on Natural Computation, ICNC 2007, 2007, v3, pp.411-415.
Carvalho Mareio, Ludermir Teresa B. Particle swarm optimization of neural network architectures and weights. Proceedings - 7th International Conference on Hybrid Intelligent Systems, HIS 2007, 2007, pp.336-339.
Jin Xin-Lei, Ma Long-Hua, Wu Tie-Jun et al. Convergence analysis of the particle swarm optimization based on stochastic processes. Zidonghua Xuebao/Acta Automatica Sinica, v33, n12, December, 2007, pp.1263-1268.
Lin Chuan, Feng Quanyuan .The standard particle swarm optimization algorithm convergence analysis and parameter selection. Proceedings - Third International Conference on Natural Computation, ICNC 2007, v3, 2007, pp.823-826.
M. Jiang, Y. P. Luo, S. Y. Yang. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters, v102, n1, Apr 15, 2007, pp.8-16.
A. Ratnaweera, S. Halgamuge and H. Watson. Selforganizing hierarchical particle swarm optimizer with time varying accelerating coefficients. IEEE Trans. Evol. Comput., vol.8, Jun. 2004, pp.240–255.
Monson C K, Sepp K D. The Kalman Swarm-A New Approach to Particle Motion in Swarm Optimization. Proceedings of the Genetic and Evolutionary Computation Conference. Springer, 2004,140-150.
Kao Yi-Tung, Zahara Erwie. A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing Journal, v8,n2, March, 2008, pp.849-857.
Sadati Nasser, Zamani Majid, Mahdavian Hamid Reza Feyz. Hybrid particle swarm-based-simulated annealing optimization techniques. IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, 2006, pp.644-648.
Jun Sun, Wenbo Xu, Wei Fang et al. Quantum-behaved particle swarm optimization with binary encoding. 8th International Conference: Adaptive and Natural Computing Algorithms. ICANNGA 2007. Proceedings, Part I (Lecture Notes in Computer Science Vol. 4431), 2007, pp.376-385.
B . Mozafari, A. M. Ranjbar, T. Amraee et al. A hybrid of particle swarm and ant colony optimization algorithms for reactive power market simulation. Journal of Intelligent and Fuzzy Systems, v17, n6, 2006, pp.557-574.
S. L. Song, L. Kong, J. J. Cheng. A Novel Stochastic Mutation Technique for Particle Swarm Optimization. Dynamics of Continuous Discrete & Impulsive System, 2007,14, pp. 500-505.
S. L. Song, L. Kong, P. Zhang. Improved particle swarm optimization algorithm with accelerating factor. Journal of Harbin Institute of Technology (New Series), January, 2007, 14(ns2), pp. 146-149.
J. S. Wu. Material Balance Computation in Alumina production process of Bayer and Series-to-parallel. Metallurgical Industry Press, Beijing, 2002. (in chinese)
S. W. Bi. Alumina Production Process. Chemical Industry Press, Beijing, 2006. (in chinese).