Modified opposition-based differential evolution for function optimization

Journal of Computational Information Systems

Volumn 7, Issue 5, 2011, Pages 1582-1591

  Xu, Qingzheng ^a,b   Wang, Lei ^a   He, Baomin ^a   Wang, Na ^b  

^a XI'AN UNIVERSITY OF TECHNOLOGY   (China)

^b XI AN COMMUNICATION INSTITUTE   (China)

Author keywords

Current optimum; Differential evolution; Function optimization; Global optimum; Opposition based learning

Indexed keywords

CURRENT OPTIMUM; DIFFERENTIAL EVOLUTION; FUNCTION OPTIMIZATION; GLOBAL OPTIMUM; OPPOSITION-BASED LEARNING;

OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 79957643966     PISSN: 15539105     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. R. Storn, and K. Price, Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Berkeley, USA, 1995.
2. R. Storn, and K. Price, Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, Vol.11, No.4, pp.341-359, 1997.
3. K.V. Price, R.M. Storn, and J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. New York: Springer, 2005.
4. J. Vesterstroem, and R. Thomsen, A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. IEEE Congress on Evolutionary Computation, Portland, USA, pp.1980-1987, 2004.
5. J. Andre, P. Siarry, and T. Dognon, An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advance in Engineering Software, Vol.32, No.1, pp.49-60, 2001.
6. O. Hrstka, and A. Kucerova, Improvement of real coded genetic algorithm based on differential operators preventing premature convergence. Advance in Engineering Software, Vol.35, No.3/4, pp.237-246, 2004.
7. H.R. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria, pp. 695-701, 2005.
8. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, Opposition versus randomness in soft computing techniques. Applied Soft Computing, Vol.8, No.2, pp.906-918, 2008.
9. H.R. Tizhoosh, Opposition-based reinforcement learning. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.4, pp.578-585, 2006.
10. Z.Y. Lin, and L.L. Wang, A new opposition-based compact genetic algorithm with fluctuation. Journal of Computational Information Systems, Vol.6, No.3, pp.897-904, 2010.
11. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, Opposition-based differential evolution algorithms. IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp.2010-2017, 2006.
12. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, Vol.12, No.1, pp.64-79, 2008.
13. M. Ventresca, and H.R. Tizhoosh, Improving the convergence of backpropagation by opposite transfer functions. International Joint Conference on Neural Networks, Vancouver, Canada, pp. 4777-4784, 2006.
14. M. Ventresca, and H.R. Tizhoosh, Opposite transfer functions and backpropagation through time. IEEE Symposium on Foundations of Computational Intelligence, Honolulu, USA, pp.570-577, 2007.
15. J. Montgomery, and M. Randall, Anti-pheromone as a tool for better exploration of search space. Third International Workshop on Ant Algorithms, Brussels, Belgium, pp. 100-110, 2002.
16. A.R. Malisia, and H.R. Tizhoosh, Applying opposition-based ideas to the ant colony system. IEEE Swarm Intelligence Symposium, Honolulu, USA, pp.182-189, 2007.
17. F. Khalvati, H.R. Tizhoosh, and M.D. Aagaard, Opposition-based window memoization for morphological algorithms. IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, USA, pp.425-430, 2007.
18. M. Ventresca, and H.R. Tizhoosh, Simulated annealing with opposite neighbors. IEEE Symposium on Foundations of Computational Intelligence, Honolulu, USA, pp.186-192, 2007.
19. L. Han, and X.S. He, A novel opposition-based particle swarm optimization for noisy problems. Third International Conference on Natural Computation, Haikou, China, pp.624-629, 2007.
20. H. Wang, Y. Liu, S.Y. Zeng, H. Li, and C.H. Li, Opposition-based particle swarm algorithm with cauchy mutation. IEEE Congress on Evolutionary Computation, Singapore, pp.4750-4756, 2007.
21. H.R. Tizhoosh, Opposite fuzzy sets with applications in image processing. Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal, pp.36-41, 2009.
22. F.S. Al-Qunaieer, H.R. Tizhoosh, and S. Rahnamayan, Oppositional fuzzy image thresholding. IEEE International Conference on Fuzzy Systems, Barcelona, Spain, pp.1-7, 2010.
23. M. Ergezer, D. Simon, and D.W. Du, Oppositional biogeography-based optimization. IEEE International Conference on Systems, Man and Cybernetics, San Antonio, USA, pp.1009-1014, 2009.
24. A. Bhattacharyaa, and P.K. Chattopadhyay, Solution of economic power dispatch problems using oppositional biogeography-based optimization. Electric Power Components and Systems, Vol.38, No.10, pp.1139-1160, 2010.
25. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, Quasi-oppositional differential evolution. IEEE Congress on Evolutionary Computation, Singapore, 2229-2236, 2007.
26. C. Zhang, Z.W. Ni, Z.J. Wu, and L.C. Gu, A novel swarm model with quasi-oppositional particle. International Forum on Information Technology and Applications, Chengdu, China, pp.325-330, 2009.
27. J. Tang, and X.J. Zhao, An enhanced opposition-based particle swarm optimization. WRI Global Congress on Intelligent Systems, Xiamen, China, pp.149-153, 2009.
28. H. Wang, Z.J. Wu, Y. Liu, J. Wang, D.Z. Jiang, and L.L. Chen, Space transformation search: A new evolutionary technique. ACM/SIGEVO Summit on Genetic and Evolutionary Computation, Shanghai, China, pp. 537-544, 2009.