A hybrid variants particle swarm optimization algorithm

2010 International Conference on Machine Vision and Human-Machine Interface, MVHI 2010

Volumn , Issue , 2010, Pages 33-36

  Zhao, Xiaojuan ^a  

^a Hunan Urban Construction College   (China)

Author keywords

Global local best; Hybrid variants; Particle swarm optimization; Root mean square

Indexed keywords

BEST VALUE; CROSS-OVER; GENETIC ALGORITHM OPERATORS; GLOBAL-LOCAL; HYBRID VARIANTS; NEW APPROACHES; PARTICLE SWARM; PARTICLE SWARM OPTIMIZATION ALGORITHM; ROOT MEAN SQUARE; STATISTICAL ANALYSIS;

ALGORITHMS; COMPUTER VISION; CONVERGENCE OF NUMERICAL METHODS; ERGONOMICS; MAN MACHINE SYSTEMS; MATHEMATICAL OPERATORS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77956428198     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MVHI.2010.80     Document Type: Conference Paper

References (15)

1. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942-1948, doi:10.1109/ICNN.1995.488968.
2. E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York, 1999.
3. J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in: Proceedings of the 1999, Congress of Evolutionary Computation, vol.3, IEEE Press, 1999, pp.1931-1938.
4. P.N. Suganthan, Particle swarm optimizer with neighborhood operator, in:Proceedings of the 1999 Congress of Evolutionary Computation, vol.3,IEEE Press, 1999, pp. 1958-1962.
5. Y. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization, in: Evolutionary Programming VII, Lecture Notes in Computer Science, vol.1447, Springer, 1998, pp. 591-600.
6. Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 4-9, 1998.
7. M. Senthil Arumugam, M.V.C. Rao, Novel hybrid approach for real coded genetic algorithm to compute the optimal control of a single stage hybrid manufacturing systems, Int. J. Comput. Intell. 1 (3) (2004) 231-249.
8. M. Senthil Arumugam, M.V.C. Rao, R. Palaniappan, New hybrid genetic operators for real coded genetic algorithm to compute the optimal control of a class of hybrid systems, Applied Soft Computing, vol. 6, no. November (1), Elsevier, 2005, pp. 38-52.
9. R.C. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization, in: Evolutionary Programming VII, Lecture Notes in Computer Science, vol.1447, Springer, 1998, pp. 611- 616.
10. X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput. 3 (2) (1999) 82-102.
11. A. Ratnaweera, S.K. Halgamuge, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient, IEEE Trans. Evol. Comput. vol.8,no. June (3) ,2004),pp. 240-255.
12. P.J. Angeline, Using selection to improve particle swarm optimization, in: Proceedings of IJCNN'99, Washington, USA, (1999), pp. 84-89.
13. M. Senthil Arumugam, M.V.C. Rao, A. Chandramohan, Competitive approaches to PSO algorithms via new acceleration coefficient variant with mutation operators, in: Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), IEEE Comp. Sci. Press, August 2005, pp. 225-230.
14. M. Lovbjerg, T.K. Rasmussen, T. Krink, Hybrid particle swarm optimizer with breeding and subpopulations, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, 2001.336
15. M.S. Arumugam, M.V.C. Rao / Applied Soft Computing 8 (2008) 324-336