An external partial permutations memory for ant colony optimization

Lecture Notes in Computer Science

Volumn 3448, Issue , 2005, Pages 1-11

  Acan, Adnan ^a  

^a EASTERN MEDITERRANEAN UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ITERATIVE METHODS; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

ANT COLONY OPTIMIZATION; OPTIMIZATION ALGORITHM; PARTIAL PERMUTATION MEMORY; VARIABLE-SIZE PARTIAL PERMUTATION;

STORAGE ALLOCATION (COMPUTER);

EID: 24644500263     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31996-2_1     Document Type: Conference Paper

References (19)

1. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for distributed discrete optimization, Artificial Life, Vol. 5, (1999), 137-172.
2. Dorigo, M., Caro, G.D.,: The ant colony optimization metaheuristic, In Corne, D., Dorigo, M., Glover, F. (eds.): New ideas in optimization, McGraw-Hill, London, (1999), 11-32.
3. Eggermont, J., Lenaerts, T.: Non-stationary function optimization using evolutionary algorithms with a case-based memory, Technical report, Leiden University Advanced Computer Science (LIACS) Technical Report 2001-11.
4. Ramsey, C.L., Grefenstette, J. J.: Case-based initialization of GAs, in Forest, S., (Editor): Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA, (1993), 84-91.
5. Louis, S., Li, G.: Augmenting genetic algorithms with memory to solve traveling salesman problem, Proceedings of the Joint Conference on Information Sciences, Duke University, (1997), 108-111.
6. Simoes, A., Costa, E.: Using genetic algorithms to deal with dynamic environments: comparative study of several approaches based on promoting diversity, in W. B. Langton et al. (eds.): Proceedings of the genetic and evolutionary computation conference GECCO'02, Morgan Kaufmann, New York, (2002), 698.
7. Simoes, A., Costa, E.: Using biological inspiration to deal with dynamic environments, Proceedings of the seventh international conference on soft computing MENDEL'2001, Czech Republic, (2001).
8. Acan, A., Tekol, Y.: Chromosome reuse in genetic algorithms, in Cantu-Paz et al. (eds.): Genetic and Evolutionary Computation Conference GECCO 2003, Springer-Verlag, Chicago, (2003), 695-705.
9. Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems, in Eiben, A. E., Back, T., Schoenauer, M., Schwefel, H. (Editors): Parallel Problem Solving from Nature- PPSN V, Berlin, (1998), 139-148.
10. Montgomery, J., Randall, M: The accumulated experience ant colony for the traveling salesman problem, International Journal of Computational Intelligence and Applications, World Scientific Publishing Company, Vol. 3, No. 2, (2003), 189-198.
11. Guntsch, M., Middendorf, M.: A population based approach for AGO, in S. Cagnoni et al., (eds.): Applications of Evolutionary Computing - EvoWorkshops2002, Lecture Notes in Computer Science, No:2279, Springer Verlag, (2002), 72-81.
12. Guntsch, M., Middendorf, M.: Applying population based AGO for dynamic optimization problems, in M. Dorigo et al., (eds.): Ant Algorithms - Third International Workshop ANTS2002, Lecture Notes in Computer Science, No:2463, Springer Verlag, (2002), 111-122.
13. A. Acan. An External Memory Implementation in Ant Colony Optimization. In Ant Colony Optimization and Swarm Intelligence - ANTSS004, page 73-82, Brussels, September 2004.
14. Stützle, T., Fernandas, S.: New benchmark instances for the QAP and the experimental analysis of algorithms. In: Gottlieb, J., Raidi, G. R. (eds.): Evolutionary Computation in combinatorial Optimization - EvoCOP 2004, Springer-Verlag, (2004), 199-209.
15. Stützle, T., Dorigo, M.: AGO Algorithms for the Quadratic Assignment Problem. In: Corne, D., Dorigo, M., Glover, F. (eds.): New Ideas in Optimization, McGraw-Hill, (1999), 33-50.
16. Stützle, T.: MAX-MIN ant system for the quadratic assignment problem. Technical Report AIDA-97-04, Darmstadt University of Technology, Computer Science Dept., Intellectics Group, (1997).
17. Stützle, T.: Iterated local search for the quadratic assignment problem. Technical Report AIDA-99-03, Darmstadt University of Technology, Computer Science Dept., Intellectics Group, (1999).
18. Maniezzo, V.: Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4), (1999), 358-369.
19. Maniezzo, V., Colorni, A., Dorigo, M.: The ant system applied to the quadratic assignment problem. Technical Report IRIDIA/94-28, Universite Libre de Bruxelles, (1994).