A new charged ant colony algorithm for continuous dynamic optimization

Applied Mathematics and Computation

Volumn 197, Issue 2, 2008, Pages 604-613

  Tfaili, Walid ^a   Siarry, Patrick ^a  

^a UNIVERSITÉ PARIS EST CRÉTEIL   (France)

Author keywords

Ant colony optimization; Continuous optimization; Dynamic optimization

Indexed keywords

DYNAMIC PROGRAMMING; ELECTROSTATICS; GAUSSIAN DISTRIBUTION; OPTIMIZATION; PROBLEM SOLVING;

ANT COLONY OPTIMIZATION; CONTINUOUS OPTIMIZATION; DYNAMIC OPTIMIZATION; METAHEURISTICS;

ALGORITHMS;

EID: 39449108113     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2007.08.087     Document Type: Article

References (27)

1. Dorigo M., and Gambardella L.M. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1 1 (1997) 53-66
2. Dorigo M., and Gambardella L.M. Guest editorial special on ant colony optimization. IEEE Transactions on Evolutionary Computation 6 4 (2002) 317-319
3. J. Dréo, P. Siarry, Dynamic optimization through continuous interacting ant colony, in: M. Dorigo, et al., (Eds.), ANTS 2004. LNCS 3172, 2004, pp. 422-423.
4. Tfaili W., Dréo J., and Siarry P. Fitting of an ant colony approach to dynamic optimization through a new set of test functions. International Journal of Computational Intelligence Research 3 3 (2007) 205-218
5. J. Branke, Evolutionary Approaches to Dynamic Environments - updated survey, in: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, 2001.
6. J. Branke, Evolutionary Approaches to Dynamic Optimization Problems - Introduction and Recent Trends, in: J. Branke, (Ed.), GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, 2003.
7. Grefenstette J.J., and Ramsey C.L. An approach to anytime learning. In: Sleeman D., and Edwards P. (Eds). International Conference on Machine Learning (1992), Morgan Kaufman 189-195
8. R. Tinos, A. de Carvalho, A genetic algorithm with gene dependent mutation probability for non-stationary optimization problems, in: Congress on Evolutionary Computation, vol. 2, 2004.
9. Cobb H.G., and Grefenstette J.J. Genetic algorithms for tracking changing environments. International Conference on Genetic Algorithms (1993), Morgan Kaufman 523-530
10. T. Nanayakkara, K. Watanabe, K. Izumi, Evolving in dynamic environments through adaptive chaotic mutation, in: Third International Symposium on Artificial Life and Robotics, vol. 2, 1999, pp. 520-523.
11. Garrett S.M., and Walker J.H. Genetic algorithms: combining evolutionary and 'non'-evolutionary methods in tracking dynamic global optima. Genetic and Evolutionary Computation Conference (2002), Morgan Kaufman 359-374
12. A. Simões, E. Costa, Using biological inspiration to deal with dynamic environments, in: Proceedings of the Seventh International Conference on Soft Computing (MENDEL01), Brno, Czech Republic, 2001.
13. C.N. Bendtsen, T. Krink, Dynamic memory model for non-stationary optimization, in: Congress on Evolutionary Computation, IEEE, 2002, pp. 145-150.
14. Trojanowski K., and Michalewicz Z. Evolutionary optimization in non-stationary environments. Journal of Computer Science and Technology 1 2 (2000) 93-124
15. C.N. Bendtsen, Optimization of non-stationary problems with evolutionary algorithms and dynamic memory, Ph.D. thesis, University of Aarhus, Department of Computer Science Ny Munkegade 8000 Aarhus C, 2001.
16. Oppacher F., and Wineberg M. The shifting balance genetic algorithm: Improving the GA in a dynamic environment. Genetic and Evolutionary Computation Conference (GECCO) vol. 1 (1999), Morgan Kaufman, San Francisco 504-510
17. W. Cedeno, V.R. Vemuri, On the use of niching for dynamic landscapes, in: International Conference on Evolutionary Computation, IEEE, 1997.
18. Ursem R.K. Multinational GA optimization techniques in dynamic environments. In: Whitley D., Goldberg D., Cantu-Paz E., Spector L., Parmee I., and Beyer H.G. (Eds). Genetic and Evolutionary Computation Conference (2000), Morgan Kaufman 19-26
19. Guntsch M., and Middendorf M. Applying population based ACO to dynamic optimization problems. Third International Workshop ANTS. LNCS 2463 (2002), Springer-Verlag 111-122
20. Guntsch M., and Middendorf M. A population based approach for ACO. 2nd European Workshop on Evolutionary Computation in Combinatorial Optimization. LNCS 2279 (2002), Springer-Verlag 72-81
21. M. Guntsch, M. Middendorf, Pheromone modification strategies for ant algorithms applied to dynamic TSP, in: EvoWorkshop, 2001, pp. 213-222.
22. M. Guntsch, M. Middendorf, H. Schmeck, An ant colony optimization approach to dynamic TSP, in: L., Spector, E.D. Goodman, A. Wu, W.B. Langdon, H.M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M.H. Garzon, E. Burke, (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufman, San Francisco, California, USA, 2001, pp. 860-867.
23. D. Merkle, M. Middendorf, Studies on the dynamics of ant colony optimization algorithms, in: The Genetic and Evolutionary Computation Conference, (GECCO), New York, 2002, pp. 105-112.
24. T.M. Blackwell, Particle swarms and population diversity I: Analysis, in: J. Branke, (Ed.), GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, 2003, pp. 9-13.
25. K. Socha, ACO for continuous and mixed-variable optimization, in: M. Dorigo, et al., (Eds.) ANTS 2004, LNCS 3172, 2004, pp. 25-36.
26. Dréo J., Aumasson J.P., Tfaili W., and Siarry P. Adaptive learning search, a new tool to help comprehending metaheuristics. International Journal on Artificial Intelligence Tools 16 3 (2007) 483-505
27. Dréo J., Pétrowski A., Siarry P., and Taillard E. Metaheuristics for hard optimization methods and case studies (2005), Springer