Hybrid differential evolution algorithm with chaos and generalized opposition-based learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6382 LNCS, Issue M4D, 2010, Pages 103-111

  Wang, Jing ^a,b   Wu, Zhijian ^a   Wang, Hui ^a  

^a WUHAN UNIVERSITY   (China)

^b JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Chaos; Differential evolution; Evolutionary algorithm; Generalized opposition based learning

Indexed keywords

BASED-ON CHAOS; CHAOS; DE ALGORITHMS; DIFFERENTIAL EVOLUTION; GENERALIZED OPPOSITION-BASED LEARNING; GLOBAL CONVERGENCE; GLOBAL OPTIMUM; HYBRID DIFFERENTIAL EVOLUTION; HYBRID DIFFERENTIAL EVOLUTION ALGORITHM; LOCAL OPTIMA; MUTATION OPERATORS; OPPOSITION-BASED LEARNING; SEARCH SPACES; SEARCHING EFFICIENCY; SIMULATION RESULT;

BIOLOGY; CHAOS THEORY; LEARNING ALGORITHMS; MATHEMATICAL OPERATORS;

EVOLUTIONARY ALGORITHMS;

EID: 78649510691     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16493-4_11     Document Type: Conference Paper

References (12)

1. Storn, R., Price, K.: Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces. University of California, Berkeley (1995)
2. Lampinen, J.: A bibliography of differential evolution algorithm [EB/OL] (2002-10-14), http://www.lut.fi/~jlampinen/debiblio.htm
3. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341-359 (1997)
4. Wolpert, D. H., Macready, W. G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67-82 (1997)
5. Wang, H., Wu, Z. J., Liu, Y., Wang, J., Jiang, D., Chen, L.: Space transformation search: A new evolutionary technique. In: World Summit on Genetic and Evolutionary Computation (2009)
6. Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proc. Congr. Evol. Comput., vol. 2, pp. 1980-1987 (2004)
7. Wang, L., Zheng, D. Z., Li, Q. S.: Survey on Chaotic Optimization Methods. Journal of Computing Technology and Automation 20(2001) (Chinese)
8. May, R.: Simple mathematical models with very complicated dynamics. Nature 261, 459-467 (1976)
9. Liu, B., Wang, L., Jin, Y. H.: Improved particle swarm optimization combined with chaos. Journal of Chaos, Solitons & Fractals 25, 1261-1271 (2005) (Pubitemid 40643077)
10. Tang, K., Yao, X., Suganthan, P. N., MacNish, C., Chen, Y. P., Chen, C. M., Yang, Z.: Benchmark Functions for the CEC 2008 Special Session and Competition on LargeScale Global Optimization, Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China (2007), http://nical.ustc.edu.cn/cec08ss.php
11. Herrera, F., Lozano, M.: Workshop: Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems - A Scalability Test (2009), http://sci2s.ugr.es/programacion/workshop/Scalability.html
12. Wang, H., Wu, Z. J., Rahnamayan, S., Kang, L. S.: A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning. In: Ninth International Conference on Intelligent Systems Design and Applications (2009)