An improved particle swarm optimization algorithm with opposition mutation

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn , Issue , 2008, Pages 5339-5343

  Chen, Zhisheng ^a   Li, Yonggang ^b  

^a CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Adaptive; Global optimization; Opposition mutation; Particle swarm

Indexed keywords

ADAPTIVE; BENCH-MARK PROBLEMS; CONTROL AND AUTOMATION; FAST CONVERGENCE; GLOBAL OPTIMUM; GLOBAL SEARCH ABILITY; IMPROVED PARTICLE SWARM OPTIMIZATION; OPPOSITION MUTATION; OPTIMIZATION ALGORITHMS; PARTICLE SWARM; PARTICLE SWARM OPTIMIZATION ALGORITHM; SIMULATION RESULTS; TUNING PARAMETERS;

ALGORITHMS; GLOBAL OPTIMIZATION; INDUSTRIAL ENGINEERING; INTELLIGENT CONTROL; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; SOFT COMPUTING; STANDARDS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 52149118443     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2008.4593800     Document Type: Conference Paper

References (10)

1. R. Eberhart and J. Kennedy, "A New Optimizer Using Particle Swarm Theory," Proceedings of the 16th International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39-43.
2. R. Eberhart and Y. Shi, "Particle Swarm Optimization: Developments, Applications and Resources," Proceedings of the 2001 Congress on Evolutionary Computation, Piscataway, NJ, USE: IEEE, 2001, pp. 81-86.
3. Y. Shi and R. Eberhart, "Fuzzy Adaptive Particle Swarm Optimization," Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, JN, USA: IEEE: 2001,pp. 101-106.
4. M. Clerc, "The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization," Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ, USA: IEEE, 1999, pp. 1927-1930.
5. P. Angeline, "Using Selection to Improve Particle Swarm Optimization," Proceedings of the 1998 International Conference on Evolutionary Computation, New York, NY, USA: IEEE 1998, pp. 84-89.
6. L. Coelho, "Novel Gaussian quantum-behaved particle swarm optimiser applied to electromagnetic design," IET Science, Measurement and Technology, vol.1, no.5, pp. 290-294, 2007
7. H. Mitusharu, I. Atsushi and Y. Keiichiro, "Particle swarm optimization with controlled mutation," IEEJ Transactions on Electrical and Electronic Engineering, vol.2, no.2, pp. 192-194,2007
8. P. Andrews, "An Investigation into Mutation Operators for Particle Swarm Optimization," IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada: IEEE 2006, pp.1044-1051.
9. H. Tizhoosh, "Opposition-based learning: A new scheme for machine intelligence," Proceeding of Conference on Computing Intelligence, Modeling Control and Automation, Vienna, Austria, 2005, pp. 695-701.
10. S. Rahnamayan, et al., "Opposition versus randomness in soft computing techniques," Appllied Soft Computing (2007), doi:10.1016/ j.asoc. 2007.07.01 (In Press)