A hybrid improved particle swarm optimization based on dynamic parameters control and metropolis accept rule strategy

3rd International Conference on Genetic and Evolutionary Computing, WGEC 2009

Volumn , Issue , 2009, Pages 649-653

  Shi, Ruifeng ^a   Liu, Xiangjie ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Chaos initialization; Dynamic parameters control; Hybrid algorithm; Metropolis accept rule; Particle swarm optimization

Indexed keywords

ADAPTIVE PARAMETERS; CHAOS INITIALIZATION; COMPARISON STUDY; CONTROL STRATEGIES; CONVERGENCE SPEED; DYNAMIC PARAMETERS; FAST CONVERGENCE; HYBRID ALGORITHM; HYBRID ALGORITHMS; IMPROVED PARTICLE SWARM OPTIMIZATION; IMPROVED PSO; METROPOLIS RULE; NUMERICAL BENCHMARK; ON DYNAMICS; OPTIMALITY; POPULATION DIVERSITY; PSO ALGORITHMS; SPECIFIC PROBLEMS; STATISTICAL OPTIMIZATION;

CHAOS THEORY; CONVERGENCE OF NUMERICAL METHODS; HEURISTIC ALGORITHMS; INDUSTRIAL APPLICATIONS; OPTICAL COMMUNICATION; PARAMETER ESTIMATION; SIMULATED ANNEALING;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77950671576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WGEC.2009.183     Document Type: Conference Paper

References (16)

1. R.C. Eberthart, J. Kennedy, A new optimizer using particle swarm theory. in the Proceeding of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43.
2. J. Kennedy, R.C. Eberhart, Particle swarm optimization. in the Proceeding of IEEE International Conference on Neural Networks, 1995: 1942-1948.
3. H. Bai, B. Zhao, A survey on application of swarm intelligence computation to electric power system. inthe Proceeding of the Sixth World Congress on Intelligent Control and Automation 2006, WCICA 2006. Dalian, P.R.China, 2006: 7587-7591.
4. B. Yang, Y.P. Chen, Z.L. Zhao, Survey on applications of particle swarm optimization in electric power systems. in the Proceeding of IEEE International Conference on Control and Automation 2007, ICCA 2007. Guangzhou, P.R.China, 2007: 481-486.
5. J.G. Vlachogiannis, K.Y. Lee, A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems. IEEE Transactions on Power Systems, 2006, 21(4): 1718-1728.
6. C. Maurice, J. Kennedy, The Particle Swarm-Explosion, Stability, and Convergence in on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2004: 90-97.
7. D.Y. Yang, J.Y. Chen, N. Matsumoto, Particle Swarm Optimization with Adaptive Parameters, in the 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing 2007. SNPD 2007. Jul.30-Aug.1, Qingdao, China. 2007, 1: 616-621.
8. Y. Shi, R.C. Eberhart, Fuzzy adaptive particle swarm optimization, 3rd ed. in the The 2001 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2001: 101-106.
9. P. Angeline, Using selection to improve particle swarm optimization. in the Proceedings of IEEE International Conference on Evolutionary Computation, 1998: 84-89.
10. P. Tawdross, A. Konig, Local parameters particle swarm optimization. in the Proceedings of 6th International Conference on Hybrid Intelligent Systems 2006, HIS '06., Rio de Janeiro, Brazil, 2006: 52-55.
11. X. Hu, R.C. Eberhart, Multiobjective optimization using dynamic neighborhood particle swarm optimization. in the Proceedings of IEEE World Congress on Computational Intelligence, 2002: 1677-1681.
12. K. Latha, R. Rajaram, An efficient LSI based information retrieval framework using particle swarm optimization and simulated annealing approach. in the Proceedings of 16th International Conference on Advanced Computing and Communications 2008, ADCOM 2008., Dec.14-17, Chennai, India, 2008: 94-101.
13. J. van Ast, R. Babuska, B. De Schutter, A general modeling framework for swarms. in the Proceedings of IEEE Congress on Evolutionary Computation 2008, CEC 2008., Jun.1-6, Hong Kong, P.R.China, 2008: 3795-3800.
14. S. Kirkpatrick, J.C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing. Science, 1983, 220: 671-680.
15. T.P. Bagchi. Multiobjective Scheduling by Genetic Algorithms. Boston, USA: Kluwer Academic Publisher, 1999.
16. L.X. Tang, J.Y. Liu, A.Y. Rong, et al.. (2001) A review of planning and scheduling systems and methods for integrated steel production. European Journal of Operational Research, (133): 1-20.