Differential evolution for integer programming problems

2007 IEEE Congress on Evolutionary Computation, CEC 2007

Volumn , Issue , 2007, Pages 2237-2242

  Omran, Mahamed G H ^a   Engelbrecht, Andries P ^b  

^a GULF UNIVERSITY FOR SCIENCE AND TECHNOLOGY   (Kuwait)

^b UNIVERSITY OF PRETORIA   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EVOLUTION (DE); SELF-ADAPTIVE DE (SDE);

DIFFERENTIAL EQUATIONS; PROBLEM SOLVING; TOPOLOGY;

INTEGER PROGRAMMING;

EID: 77749323067     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4424749     Document Type: Conference Paper

References (18)

1. E. Laskari, K. Parsopoulos and M. Vrahatis. Particle Swarm Optimization for Integer Programming. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1582-1587,2002.
2. R. Horst and H. Tuy. Global Optimization, Deterministic Approaches, Springer, 1996.
3. A. Engelbrecht. Computational Intelligence: An Introduction. John Wiley and Sons, 2002.
4. G. Rudolph. An Evolutionary Algorithm for Integer Programming. Y. Davidoe, H. Schwefel and R. Manner (eds.), Parallel Problem Solving from Nature, vol. 3, pp. 139-148, Springer, 1994.
5. R. Kelahan and J. Gaddy. Application of the Adaptive Random Search to Discrete and Mixed Integer Optimization. Intenational Journal for Numerical Methods in Engineering, vol. 12, pp. 289-298, 1978.
6. J. Kennedy and R. Eberhart. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942-1948, 1995.
7. 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.
8. Y. Lin, K. Hwang and F. Wang. Co-evolutionary Hybrid Differential Evolution for Mixed-Integer Optimization Problems. Eng. Opt., vol. 00, pp. 1-20, 2001.
9. K. Price, R. Storn and J. Lampinen. Differential Evolution: a Practical Approach to Global Optimization, Springer, Berlin, 2005.
10. D. Goldberg. Genetic Algorithms in search, optimization and machine learning, Addison-Wesley, 1989.
11. th International Parallel and Distributed Processing Symposium, pp. 165-170, 2004.
12. F. Xue, A. Sanderson and R. Graves. Pareto-based Multi-Objective Differential Evolution. In: the 2003 Congress on Evolutionary Computation, vol. 2, pp. 862-869, 2003.
13. D. Karaboga and S. Okdem. A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm. Turk Journal of Electrical Engineering, vol. 12, no. 1, pp. 53-60, 2004.
14. T. Krink, B. Filipic, B. and G. Fogel. Noisy Optimization Problems - A Particular Challenge for Differential Evolution. In: the 2004 Congress on Evolutionary Computation, pp. 332-339, 2004.
15. M. Omran, A. Salman andA. Engelbrecht. Self-Adaptive Differential Evolution. The 2005 International Conference on Computation intelligence and Security (co-sponsored by IEEE and Springer), December, Xi'an, China, Springer's Lecture Notes in Artificial Intelligence, vol. 3801,pp. 192-199, 2005.
16. M. Omran, A. Engelbrecht and A. Salman. Using Neighborhood Topologies with Differential Evolution. In the Workshop Proceedings of the 2005 International Conference on Computation intelligence and Security, December, Xi'an, China, 2005.
17. K. Price and R. Storn. DE Web site, http://www.ICSI.Berkeley.edu/~storn/ code.html (visited 8 Aug 2006), 2006.
18. M. Clerc and J. Kennedy. The particle swarm: Explosion, Stability and Convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, vol. 6, pp. 58-73, 2002.