Using opposition-based learning to improve the performance of particle swarm optimization

2008 IEEE Swarm Intelligence Symposium, SIS 2008

Volumn , Issue , 2008, Pages

  Omran, Mahamed G H ^a   Al Sharhan, Salah ^a  

^a GULF UNIVERSITY FOR SCIENCE AND TECHNOLOGY   (Kuwait)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR AUTOMATA; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; SURVEYING INSTRUMENTS; SWITCHING CIRCUITS;

BENCHMARK FUNCTIONS; COMPUTATIONALLY EXPENSIVE; OPTIMIZATION METHODS; PARTICLE SWARMS; PREMATURE CONVERGENCES;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 57649178201     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SIS.2008.4668288     Document Type: Conference Paper

References (16)

1. R. Eberhart and J. Kennedy. A New Optimizer using Particle Swarm Theory. In Proceedings of the Sixth International Symposium on Micromachine and Human Science, pp. 39-43, 1995.
2. L. Han and X. He. A novel Opposition-based Particle Swarm Optimization for Noisy Problems. In the Proceedings of the Third International Conference on Natural Computation, IEEE Press, vol. 3, pp. 624-629, 2007.
3. J. Kennedy and R.C. Eberhart. Particle Swarm Optimization. In Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1.942-1.948, IEEE Press, 1995.
4. A. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley & Sons, 2005.
5. J. Kennedy and R.C. Eberhart and Y. Shi. Swarm Intelligence. Morgan Kaufmann, 2001.
6. H. Tizhoosh. Opposition-based Learning: A New Scheme for Machine Intelligence. In Proc. Int. Conf. Comput. Intell. Modeling Control and Autom, Vienna, Austria, vol. I, pp. 695-701, 2005.
7. S. Rahnamayan, H. Tizhoosh and M. Salama. Opposite-based Differential Evolution. IEEE Trans. On Evolutionary Computation, vol. 12(1), pp. 107 - 125, 2008.
8. R. Storn and K. Price. Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, 1995.
9. Y. Shi and R. Eberhart. A Modified Particle Swarm Optimizer. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 69 - 73, 1998.
10. J. Kennedy. Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, vol. 3, pp. 1931 - 1938, 1999.
11. J. Kennedy and R. Mendes. Population Structure and Particle Performance. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1671 - 1676, IEEE Press, 2002.
12. E. Peer and F. van den Bergh and A. Engelbrecht. Using Neighborhoods with the Guaranteed Convergence PSO. In Proceedings of the IEEE Swarm Intelligence Symposium, pp. 235 - 242, IEEE Press, 2003.
13. M. Clerc and J. Kennedy. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, vol. 6(1), pp. 58 - 73, 2002.
14. F. van den Bergh. An Analysis of Particle Swarm Optimizers. Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.
15. F. van den Bergh and A. Engelbrecht. A Study of Particle Swarm Optimization Particle Trajectories. Information Sciences, vol. 176(8), pp. 937 - 971, 2006.
16. H. Wang, Y. Liu, S. Zeng, H. Li, C. Li. Opposition-based Particle Swarm Algorithm with Cauchy Mutation. In the Proceedings of the IEEE Congress on Evolutionary Computation, pp. 4750-4756, IEEE Press, 2007.