Ant colony optimization for multi-objective optimization problems

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 1, Issue , 2007, Pages 450-457

  Alaya, Inès ^a   Solnon, Christine ^b   Ghédira, Khaled ^a  

^a UNIVERSITY OF MANOUBA   (Tunisia)

^b UNIVERSITÉ DE LYON   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; COMBINATORIAL OPTIMIZATION; COMPUTER PROGRAMMING;

ANT COLONIES; GENERIC ALGORITHM; KNAPSACK PROBLEMS; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; PARAMETERIZED; PHEROMONE TRAILS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 48649100088     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2007.108     Document Type: Conference Paper

References (21)

1. I. Alaya, C. Solnon, K. Ghédira, Ant algorithm for the multi-dimensional knapsack problem, Proceedings of International Conference on Bioinspired Optimization Methods , their Applications (BIOMA 2004), 2004, p. 63-72.
2. B. Barán, M. Schaerer, A Multiobjective Ant Colony System for Vehicle Routing Problem with TimeWindows, Proc. Twenty first IASTED International Conference on Applied Informatics, Insbruck, Austria, 2003, p. 97-102.
3. B. Bullnheimer R.F. Hartl C. Strauss, An Improved Ant system Algorithm for the Vehicule Routing Problem, Annals of Operations Research, vol. 89, 1999, p. 319-328.
4. K. Doerner, R. F. Hartl, M. Teimann, Are COMPETants More Competent for Problem Solving? The Case of Full Truckload Transportation, Central European Journal of Operations Research, vol. 11, n2, 2003, p. 115-141.
5. K. Doerner, W. J. Gutjahr, R. F. Hartl, C. Strauss, C. Stummer, Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection, Annals of Operations Research, 2004.
6. M. Dorigo, Optimization, Learning, Natural Algorithms (in Italian), PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992.
7. M. Dorigo, T. Stüzle, Ant Colony Optimization, MIT Press, 2004.
8. C.M. Fonseca, P.J. Fleming, Genetic algorithms for multi-objective optimization: formulation, discussion, generalization, The Fifth International Conference on Genetic Algorithms, 1993, p. 416-423.
9. L. Gambardella, E.D. Taillard, G. Agazzi, MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows, in F. G. D. Corne, M. Dorigo (ed.), New Ideas in Optimization, McGraw Hill, London, UK, 1999, p. 63-76.
10. M. Gravel, W. L. Price, C. Gagné, Scheduling Continuous Casting of Aluminium using a Multiple Objective Ant Colony Optimization Metaheuristic, European Journal of Operational Research, vol. 143, n1, 2002, p. 218-229.
11. P. Hajela, C-Y. Lin, Genetic search strategies in multicriterion optimal design, Structural Optimization, n 4, 1992, p. 99-107.
12. J. Horn, N. Nafpliotis, D.E. Goldberg, A niched pareto genetic algorithm for multiobjective optimization, in I. S. Center (ed.), First IEEE Conference on Evolutionary Computation, vol. 1, IEEE World Congress on Computational Computation, Piscataway, 1994, p. 82-87.
13. S. Iredi, D. Merkle, M. Middendorf, Bi-Criterion Optimization with Multi Colony Ant Algorithms, First International Conference on Evolutionary Multi-criterion Optimization (EMO'01), vol. 1993, Lecture Notes in Computer Science, 2001, p. 359-372.
14. C. E. Mariano, E. Morales, A Multiple Objective Ant-Q Algorithm for the Design of Water Distribution Irrigation Networks, Technical Report n HC-9904, Instituto Mexicano de Tecnologa del Agua, Mexico, June, 1999.
15. P. R. McMullen, An Ant Colony Optimization Approach to Addressing a JIT Sequencing Problem with Multiple Objectives, Artificial Intelligence in Engineering, vol. 15, n3, 2001, p. 309-317.
16. J. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in J. Grefenstette (ed.), ICGA International Conference on Genetic Algorithms, Lecture Notes in Computer Science, 1985, p. 93-100.
17. N. Srinivas, K. Deb, Multiobjective optimization using non dominated sorting in genetic algorithms, Evolutionary Computation, vol. 2, 1994, p. 221-248.
18. C. Solnon, Ants can Solve Constraint Satisfaction Problems , IEEE Transactions on Evolutionary Computation, vol. 6, n4, 2002, p. 347-357.
19. C. Solnon, S. Fenet, A study of ACO capabilities for solving the Maximum Clique Problem, Journal of Heuristics, vol. 12, n3, 2006, p. 155-180.
20. T. Stützle, H. Hoos, MAX-MIN Ant System, Journal of Future Generation Computer Systems, vol. 16, 2000, p. 889-914.
21. E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study, the strength Pareto approach, IEEE Transactions on Evolutionary Computation, n 3, 1999, p. 257-271.