An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms

Advances in Engineering Software

Volumn 40, Issue 5, 2009, Pages 341-349

  Uǧur, Aybars ^a   Aydin, Doǧan ^a  

^a EGE UNIVERSITY   (Turkey)

Author keywords

Ant Colony Optimization; Combinatorial optimization, visualization; Local search heuristics; Simulation software; Traveling salesman problem

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; COMBINATORIAL OPTIMIZATION; COMPUTER SOFTWARE; CONSTRAINED OPTIMIZATION; HEURISTIC ALGORITHMS; HEURISTIC METHODS; OPTIMIZATION; PROBLEM SOLVING; VISUALIZATION;

ACO ALGORITHMS; ANT COLONY OPTIMIZATION; ANT-COLONY OPTIMIZATION ALGORITHMS; BENCH-MARK PROBLEMS; COMBINATORIAL OP-TIMIZATION PROBLEMS; COMBINATORIAL OPTIMIZATION, VISUALIZATION; INTERACTIVE SIMULATIONS; INTERACTIVE VISUALIZATIONS; LOCAL SEARCH HEURISTICS; OPTIMIZATION PROBLEMS; REAL-TIME ANALYSIS; SHORTEST ROUTES; SIMULATION SOFTWARE; TEST RESULTS; WEB-BASED SIMULATIONS;

TRAVELING SALESMAN PROBLEM;

EID: 60249086880     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2008.05.004     Document Type: Article

References (48)

1. Kureichik V.V., and Kureichik V.M. A genetic algorithm for finding a salesman's route. J Comput Syst Sci Int 45 1 (2006) 89-95
2. Goldberg D.E. Genetic algorithms in search, optimization and machine learning (1989), Addison-Wesley, Reading, MA
3. Laarhoven P.V., and Aarts E.H.L. Simulated annealing: theory and applications (1987), Kluwer Academic Publishers
4. Fiechter L. A parallel tabu search algorithm for large traveling salesman problems. Dis Appl Math Combinat Operat Res Comput Sci 51 (1994) 243-267
5. Dorigo M, Maniezzo V, Colorni A. Positive feedback as a search strategy. Technical report, Dipartimento di Elettronica, Politecnico di Milano; 1991.
6. Dorigo M., Maniezzo V., and Colorni A. The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cyber Part-B 26 (1996) 29-41
7. Dorigo M., and Gambardella L.M. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1 (1997) 53-66
8. Stützle T., and Dorigo M. A short convergence proof for a class of ant colony optimization algorithms. IEEE Trans Evol Comput 6 4 (2002) 358-365
9. Jin H.D., Leung K.S., Wong M.L., and Xu Z.B. An efficient self-organizing map designed by genetic algorithms for the traveling salesman problem. IEEE Trans Syst Man Cyber Part-B 33 6 (2003) 877-887
10. Applegate D., Bixby R., Chvátal V., and Cook W. On the solution of traveling salesman problems. Documenta Math Proc Int Cogr Math 3 (1998) 645-656
11. Baraglia R., Hidalgo J.I., and Perego R. A hybrid heuristic for the traveling salesman problem. IEEE Trans Evol Comput 5 (2001) 613-622
12. Padberg M., and Rinaldi G. Optimization of a 532-city symmetric genetic traveling salesman problem by branch and cut. Oper Res Lett 6 1 (1987) 1-7
13. Kuan S.N., Ong H.L., and Ng K.M. Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Adv Eng Software 37 6 (2006) 351-359
14. Coelho L.D.S., and Mariani V.C. Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34 3 (2008) 1905-1913
15. Foong W.K., Simpson A.R., Maier H.R., et al. Ant colony optimization for power plant maintenance scheduling optimization - a five-station hydropower system. Ann Oper Res 159 1 (2008) 433-450
16. Shelokar P.S., Jayaraman V.K., and Kulkarni B.D. An ant colony classifier system: application to some process engineering problems. Comput Chem Eng 28 9 (2004) 1577-1584
17. Samrout M., Kouta R., Yalaoui F., et al. Parameter's setting of the ant colony algorithm applied in preventive maintenance optimization. J Int Manuf 18 6 (2007) 663-677
18. Liao Y.H., and Sun C.T. An educational genetic algorithms learning tool. IEEE Trans Education 44 (2001) 20
19. Collins T.D. Applying software visualization technology to support the use of evolutionary algorithms. J Visual Languages Comput 14 (2003) 123-150
20. Kollat J.B., and Reed P. A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO). Environ Modell Software 22 (2007) 1691-1704
21. Ringwood J.V., and Galvin G. Computer-aided learning in artificial neural networks. IEEE Trans Education 45 (2002) 380-387
22. Manic M, Wilamowsky B, Malinowsky A. Internet based neural network simulation tool. In: Proceeding of the IECON'02, vol. 4; 2002. p. 2870-4.
23. Rami{dotless}́rez J.A., Guimaraes F.G., Campelo F., Pereira E.C., Barros P.H.L., Takahashi R.H.C., et al. Optimise: a computational environment for teaching optimization in electrical engineering. IEEE Trans Magn 40 (2004) 695-698
24. Reiners T., and Vob T. Teaching meta-heuristics within virtual learning environments. Int Trans Oper Res 11 (2004) 225-238
25. Foulloy L., Boukezzoula R., and Galichet S. An educational tool for fuzzy control. IEEE Trans Fuzzy Syst 14 (2006) 217-221
26. Effective Interactive AI Resources Workshop. IJCAI; 2001.
27. Symposium on Improving Instruction of Introductory AI. AAAI; 1994.
28. Dorigo M. Ottimizzazione, Apprendimento Automatico, ed Algoritmi Basati su Metafora Naturale. Ph.D. thesis, Politecnico di Milano; 1992.
29. Dorigo M., and Di Caro G. Ant colony optimization: a new meta-heuristic. In: Angeline P.J., Michalewicz Z., Schoenauer M., Yao X., and Zalzala A. (Eds). Proceedings of the congress on evolutionary computation vol. 2 (1999), IEEE Press 1470-1477
30. Dorigo M, Birattari M, Stützle T. Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Comput Intell Mag; 2006. p. 28-38.
31. Costa H.A. Ants can colour graphs. J Oper Res Soc 48 (1997) 295-305
32. Leguizamón G, Michalewicz Z. A new version of ant system for subset problems. In: Proceeding of the CEC'99; 1999. p. 1459-64.
33. Solnon. Ant can solve constraint satisfaction problems. IEEE Trans Evol Comput 6 (2002) 347-357
34. Campos L.M.D., Fernández-Luna J.M., Gámez J.A., and Puetra J.M. Ant colony optimization for learning bayesian network. Int J Approx Reasoning 31 (2002) 291-311
35. Lessing L, Dumitrescu I, Stützle T. A comparison between ACO algorithms for the set covering problem. In: Proceeding of the ANTS'2004, ser. LNCS 3172; 2004. p. 1-12.
36. Reimann M., Doerner K., and Hartl R.F. D-ants: saving based ants divide and conquer the vehicle routing problem. Comput Oper Res 31 (2004) 237-255
37. Korb O, Stützle T, Exner TE. Application of ant colony optimization to structure-based drug design. In: Proceeding of the ANTS 2006, ser. LNCS 4150; 2006. p. 247-58.
38. Grassé P.P. La reconstruction dun id et les coordinations interindividuelles chez bellicosimetes natalensis et cubitemes sp. la théorie de la stigmerie: essai d'Inrprétation du comportement des termites constructeurs. Insectes Sociaux 6 (1959) 41-81
39. Lin S. Computer solutions for the traveling salesman problem. Bell Syst J 44 (1965) 2245-2269
40. Lin S., and Kernighan B.W. An effective heuristic algorithm for the travelling salesman problem. Oper Res 21 (1973) 498-516
41. Dorigo M., and Gambardella L.M. Ant colonies for the traveling salesman problem. Bio Syst 43 (1997) 73-81
42. Bullnheimer, Hartl R.F., and Strauss C. A new ranked-based version of the ant system: a computational study. Central Eur J Oper Res 7 (1999) 25-38
43. Cordon O, Viana IF, Moreno L. New ACO model integrating evolutionary computation concepts: the best-worst ant system. In: Proceeding of the ANTS 2000, Brussels; 2000. p. 22-9.
44. Stützle T., and Hoos H.H. Max-min ant system. Future Generation Comput Syst 16 (2000) 889-914
45. Shtovba S.D. Ant algorithms: theory and applications. Program Comput Software 31 (2005) 167-178
46. Bentley J.L. Fast algorithms for geometric traveling salesman problems. ORSA J Comput 4 (1992) 387-411
47. Johnson D.S., and McGeoch L.A. The traveling salesman problem: a case study in local optimization. In: Aarts E.H.L., and Lenstra J.K. (Eds). Local search in combinatorial optimization (1995), Wiley and Sons
48. Bonabeau E., Dorigo M., and Theraulaz G. Swarm intelligence from natural to artificial systems (1999), Oxford University Press, New York