GRACE: A generational randomized ACO for the multi-objective shortest path problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6576 LNCS, Issue , 2011, Pages 535-549

  Bezerra, Leonardo C T ^a   Goldbarg, Elizabeth F G ^a   Buriol, Luciana S ^b   Goldbarg, Marco C ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

^b FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

Author keywords

Ant Colony Optimization; Multi objective; Shortest Path Problem

Indexed keywords

ACO ALGORITHMS; ANT COLONY OPTIMIZATION ALGORITHMS; ANT-COLONY OPTIMIZATION; COMPUTATIONAL EXPERIMENT; EXACT ALGORITHMS; HIGH QUALITY; META HEURISTICS; MULTI-OBJECTIVE; MULTI-OBJECTIVE SHORTEST PATH PROBLEMS; NP-HARD PROBLEM; NSGA-II; SHORTEST PATH PROBLEM; MULTI OBJECTIVE;

COMPUTATIONAL COMPLEXITY; EVOLUTIONARY ALGORITHMS; GRAPH THEORY; MULTIOBJECTIVE OPTIMIZATION; ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; PROBLEM SOLVING;

PROBLEM SOLVING; OPTIMIZATION;

EID: 79953823551     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19893-9_37     Document Type: Conference Paper

References (38)

1. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - A platform and programming language independent interface for search algorithms. In: Fonseca, C., Fleming, P., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494-508. Springer, Heidelberg (2003)
2. Buriol, L.S., Resende, M.G.C., Thorup, M.: Speeding up dynamic shortest path algorithms. INF. J. on Comp. 20(2), 191-204 (2008)
3. Conover, W.J.: Practical non-parametric statistics, 3rd edn. John Wiley and Sons, New York (1999)
4. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press and McGraw-Hill (2001)
5. Costelloe, D., Mooney, P., Winstansley, A.: From random walks to pareto optimal paths. In: Proceedings of the 4th Irish Artificial Intelligence and Cognitive Science Conference, pp. 523-530 (2001)
6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. on Evol. Comp. 6(2), 182-197 (2002)
7. Dijkstra,E.W.:Anote on two problems in connexion with graphs.Num.Math. (1959)
8. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evol. Comp. 1(1), 53-66 (1997)
9. Dorigo, M., Socha, K.: An introduction to ant colony optimization. Tech. Rep. TR/IRIDIA/2006-010, Institut de Recherches Interdisciplinaires et de Développements em Intelligence Artificialle, Université Libre de Bruxelles (2006)
10. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Dipartamento de Elettronica, Politecnico de Milano (1992)
11. Ehrgott, M., Gandibleux, X.: A survey and annotated bibliography of multiobjective combinatorial optimization. OR Spek. 22(4), 425-460 (2000)
12. Ghoseiri, K., Nadjari, B.: An ant colony optimization algorithm for the bi-objective shortest path problem. App. Soft Comp. 10(4), 1237-1246 (2010)
13. Häckel, S., Fischer, M., Zechel, D., Teich, T.: A multi-objective ant colony approach for pareto-optimization using dynamic programming. In: GECCO 2008, pp. 33-40. ACM, New York (2008)
14. He, F., Qi, H., Fan, Q.: An evolutionary algorithm for the multi-objective shortest path problem. In: Proceedings of ISKE 2007 (2007)
15. Iredi, S., Merkle, D., Middendorf, M.: Bi-criterion optimization with multi colony ant algorithms. In: Zitzler, E., Thiele, L., Deb, K., Coello, C.A.C., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 359-372. Springer, Heidelberg (2001)
16. Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. on Sys. Man and Cyber. Part C: App. and rev. 28(3), 392-403 (1998)
17. Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. Tech. Rep. TIK-Report No. 214, Computer Engineering and Networks Laboratory, ETH Zurich (2006)
18. Lin, L., Gen, M.: A bicriteria shortest path routing problems by hybrid genetic algorithm in communication networks. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 4577-4582 (2007)
19. López-Ibanez, M., Paquete, L., Stützle, T.: On the design of ACO for the biobjective quadratic assignment problem. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L., Montada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 214-225. Springer, Heidelberg (2004)
20. Martins, E.Q.V.: On a multicritera shortest path problem. Eur. J. of Op. Res. 16, 236-245 (1984)
21. Michael, C.M., Stewart, C.V., Kelly, R.B.: Reducing the search time of a steady state genetic algorithm using the immigration operator. In: Proceedings of the IEEE Congress on Tools for AI, pp. 500-501 (1991)
22. Mooney, P., Winstansley, A.: An evolutionary algorithm for multicriteria path optimization problems. Int. J. of Geo. Inf. Sci. 20(4), 401-423 (2006)
23. Mote, J., Murphy, I., Olson, D.L.: A parametric approach to solving bicriterion shortest path problems. Eur. J. of Op. Res. 53, 81-92 (1991)
24. MSPP: www.mat.uc.pt/~zeluis/INVESTIG/MSPP/mspp.htm
25. Multi-objective NSGA-II code in C. Revision 1.1 (June 10, 2005) (For Linux only), www.iitk.ac.in/kangal/codes.shtml
26. Murata, T., Ishibuchi, H., Gen, M.: Specification of genetic search directions in cellular multi-objective genetic algorithms. In: Zitzler, E., Thiele, L., Deb, K., Coello, C.A.C., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 82-95. Springer, Heidelberg (2001)
27. Pangilinan, J.M.A., Janseens, G.K.: Evolutionary algorithms for the multiobjective shortest path problem. Int. J. of Comp. and Inf. Sci. and Eng. 1 (2007)
28. Raith, A., Ehrgott, M.: A comparison of solution strategies for the biobjective shortest path problem. Comp. and Op. Res. 36(4), 1299-1331 (2009)
29. Rocha, D.A.M.: Busca local para o problema quadrático de alocação multiobjetivo (pqa-mo). Final essay for Computer Science B.Sc. Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil (2006)
30. Serafini, P.: Some considerations about computational complexity for multi objective combinatorial problems. In: Jahn, J., Krabs, W. (eds.) Recent advances and historical development of vector optimization. Lecture Notes in Economics and Mathematical Systems, vol. 294. Springer, Berlin (1986)
31. Skriver, A.J.V., Andersen, K.A.: A label correcting approach for solving bicriterion shortest-path problems. Comp. and Op. Res. 27(6), 507-524 (2000)
32. Skriver, A.J.V.: A classification of bicriterion shortest path (bsp) algorithms. Asia- Pac. J. of Op. Res. 17, 199-212 (2000)
33. Steiner, S., Radzik, T.: Solving the biobjective minimum spanning tree problem using a k-best algorithm. Tech. Rep. TR-03-06, Department of Computer Science, King's College London (2003)
34. Taillard, E., Waelti, P., Zuber, J.: Few statistical tests for proportions comparison. Eur. J. Oper. Res. 185, 1336-1350 (2009)
35. Wilcoxon, F.: Individual comparisons by ranking methods. Bio. bull. 1(6), 80-83 (1945)
36. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto evolutionary algorithm. IEEE Trans. on Evol. Comp. 3(4), 257-271 (1999)
37. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. Tech. Rep. TIK-Report No. 139, Institut für Technische Informatik und Kommunikationsnetze, ETH Zürich (2002)
38. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. Tech. Rep. TIK-Report 103, Computer Engineering and Networks Laboratory (TIK), Departament of Electrical Engeneering, Swiss Federal Institute fo Technology (ETH) Zurich (2001)