A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem

Engineering Applications of Artificial Intelligence

Volumn 21, Issue 8, 2008, Pages 1370-1380

  Xing, Ling Ning ^a   Chen, Ying Wu ^a   Yang, Ke Wei ^a   Hou, Feng ^a   Shen, Xue Shi ^a   Cai, Huai Ping ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Asymmetric traveling salesman problem; Combinatorial optimization; Heuristic approach; Improved genetic algorithm; Optimization strategy

Indexed keywords

ALGORITHMS; BOOLEAN FUNCTIONS; COMBINATORIAL MATHEMATICS; COMBINATORIAL OPTIMIZATION; GENETIC ALGORITHMS; GLOBAL OPTIMIZATION; HEURISTIC ALGORITHMS; HEURISTIC METHODS; OPTIMIZATION; PROBLEM SOLVING; SOLUTIONS;

APPLICATIONS.; ASYMMETRIC TRAVELING SALESMAN PROBLEM; ASYMMETRIC TRAVELING SALESMAN PROBLEMS; COMBINATORIAL OPTIMIZATION PROBLEMS; COMPARATIVE STUDIES; COMPUTATIONAL EXPERIMENTS; CROSSOVER OPERATIONS; EMPIRICAL OPTIMIZATIONS; HEURISTIC APPROACH; HEURISTIC APPROACHES; HYBRID APPROACHES; IMPROVED GENETIC ALGORITHM; IMPROVED GENETIC ALGORITHMS; LOCAL OPTIMIZATIONS; MUTATION OPERATIONS; OPTIMIZATION STRATEGIES; OPTIMIZATION STRATEGY; TRAVELING SALESMAN;

TRAVELING SALESMAN PROBLEM;

EID: 54049083462     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.01.003     Document Type: Article

References (62)

1. Ackley D.H., and Littman M. Interactions between learning and evolution. In: Langton C.G., Taylor C., Garmer J.D., and Rasmussen S. (Eds). Artificial Life II (1992), Addison-Wesley, Reading, MA 487-507
2. Balas E., and Toth P. Branch and bound methods. In: Lawler E.L., Lenstra J.K., Rinnooy Kan A.H.G., and Shmoys D.B. (Eds). The Traveling Salesman Problem (1985), Wiley, New York 361-402
3. Baldwin J.M. A new factor in evolution. American Naturalist 30 (1896) 441-451
4. Bean J. Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6 2 (1994) 154-160
5. Belew, R.K., 1989. When both individuals and populations search: adding simple learning to the genetic algorithm. In: Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 34-41
6. Boers, E.J.W., Borst, M.V., Sprinkhuizen-Kuyper, I.G., 1995. Evolving artificial neural networks using the 'Baldwin Effect'. In: Proceedings of the International Conference in Artificial Neural Nets and Genetic Algorithms. Springer, Ales, France, pp. 333-336.
7. Buriol L., Franca P., and Moscato P. A new memetic algorithm for the asymmetric traveling salesman problem. Journal of Heuristics 10 5 (2004) 483-506
8. Carpaneto G., and Toth P. Some new branching and bounding criteria for the asymmetric traveling salesman problem. Management Science 26 7 (1980) 736-743
9. Choi I.-C., Kim S.-I., and Kim H.-S. A genetic algorithm with a mixed region search for the asymmetric traveling salesman problem. Computers and Operations Research 30 5 (2003) 773-786
10. Choobineh F.F., Mohebbi E., and Khoo H. A multi-objective tabu search for a single-machine scheduling problem with sequence-dependent setup times. European Journal of Operational Research 175 1 (2006) 318-337
11. Chu P.C., and Beasley J.E. A genetic algorithm for the generalized assignment problem. Computers and Operations Research 24 1 (1997) 17-23
12. Climer S., and Zhang W. Cut-and-solve: an iterative search strategy for combinatorial optimization problems. Artificial Intelligence 170 8-9 (2006) 714-738
13. Corberan A., Marti R., and Sanchis J.M. A GRASP heuristic for the mixed Chinese postman problem. European Journal of Operational Research 142 1 (2002) 70-80
14. 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) 53-56
15. Dutta A., and Mitra S. Integrating heuristic knowledge and optimization models for communication network design. IEEE Transactions on Knowledge and Data Engineering 5 6 (1993) 999-1017
16. Fiechter C.-N. A parallel tabu search algorithm for large traveling salesman problems. Discrete Applied Mathematics 51 3 (1994) 243-267
17. Fischetti M., and Toth P. An additive bounding procedure for combinatorial optimization problems. Operations Research 37 2 (1989) 319-328
18. Fischetti M., and Toth P. An additive bounding procedure for the asymmetric traveling salesman problem. Mathematical Programming 53 2 (1992) 173-197
19. Fischetti M., and Toth P. A polyhedral approach to the asymmetric traveling salesman problem. Management Science 43 11 (1997) 1520-1536
20. Freisleben, B., Merz, P., 1996. Genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems. In: Proceedings of the IEEE Conference on Evolutionary Computation. IEEE Press, Nagoya, pp. 616-621.
21. Frieze A.M., Galbiati G., and Maffioli F. On the worst-case performance of some algorithms for the asymmetric traveling salesman problem. Networks 12 1 (1982) 23-39
22. Gen M., and Cheng R. Genetic Algorithms and Engineering Optimization (1999), Wiley, New York
23. Gorges-Schleuter, M., 1997. Asparagos96 and the traveling salesman problem. In: Proceedings of 1997 IEEE International Conference on Evolutionary Computation. IEEE Press, Indianapolis, IN, pp. 171-174.
24. Gouveia L., and Pires J. The asymmetric traveling salesman problem and a reformulation of the Miller-Tucker-Zemlin constraints. European Journal of Operational Research 112 1 (1999) 134-146
25. Harvey, I., 1993. The puzzle of the persistent question marks: a case study of genetic drift. In: Proceedings of the Fifth International Conference on Genetic Algorithms Morgan. Kaufmann, San Mateo, CA, pp. 15-22.
26. Helsgaun K. An effective implementation of the Lin-Kernighan traveling salesman heuristic. European Journal of Operational Research 126 1 (2000) 106-130
27. Hinton G.E., and Nowlan S.J. How learning can guide evolution. Complex Systems 1 3 (1987) 495-502
28. Holland J.H. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor
29. Johnson D.S., Gutin G., McGeoch L.A., et al. The Asymmetric Traveling Salesman Problem: Algorithms, Instance Generators and Tests (2002), Kluwer Academic Publishers, Boston, USA
30. Joines J.A., Kay M.G., King R.E., et al. A hybrid genetic algorithm for manufacturing cell design. Journal of Chinese Institute of Industrial Engineers 17 5 (2000) 549-564
31. Karp R.M. A patching algorithm for the nonsymmetric traveling-salesman problem. SIAM Journal of Computing 8 4 (1979) 561-573
32. Kusiak A. Process planning: a knowledge-based and optimization perspective. IEEE Transactions on Robotics and Automation 7 3 (1991) 257-266
33. Lysgaard J. Cluster based branching for the asymmetric traveling salesman problem. European Journal of Operational Research 119 2 (1999) 314-325
34. Merz P., and Freisleben B. Genetic local search for the TSP: new results. Proceedings of the IEEE International Conference on Evolutionary Computation (1997), IEEE Press, Indianapolis, IN 159-164
35. Miller D.L., and Pekny J.F. Results from a parallel branch and bound algorithm for the asymmetric traveling salesman problem. Operations Research Letters 8 3 (1989) 129-135
36. Miller D.L., and Pekny J.F. Exact solution of large asymmetric traveling salesman problems. Science 251 4995 (1991) 754-761
37. Moscato P. Memetic algorithms: a short introduction. In: Corne D., Dorigo M., and Glover F. (Eds). New Ideas in Optimization (1999), McGraw-Hill, London, UK
38. Moscato P., and Norman M.G. A memetic approach for the traveling salesman problem, implementation of a computational ecology for combinatorial optimization on message-passing systems. In: Valero M., Onate E., Jane M., et al. (Eds). Parallel Computing and Transputer Applications (1992), IOS Press, Amsterdam 187-194
39. Murata T., Ishibuchi H., and Tanaka H. Genetic algorithms for flow-shop scheduling problems. Computers and Industrial Engineering 30 4 (1996) 1061-1071
40. Nagata Y., and Kobayashi S. Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. Proceedings of the Seventh International Conference on Genetic Algorithms (1997), Morgan Kaufmann, IEEE Press, Los Altos, CA 450-457
41. Poon P.W., and Carter J.N. Genetic algorithm crossover operations for ordering applications. Computers and Operations Research 22 1 (1995) 135-147
42. Potvin J.Y. The traveling salesman problem: a neural network perspective. ORSA Journal on Computing 5 4 (1993) 328-348
43. Potvin J.Y. Genetic algorithm for the traveling salesman problem. Annals of Operations Research 63 (1996) 339-370
44. Reeves C.R. Modern Heuristic Techniques for Combinatorial Problems (1993), Blackwell, Oxford
45. Reinelt G. TSPLIB-a traveling salesman library. ORSA Journal on Computing 3 4 (1991) 376-384
46. Renders J.M., and Flasse S.P. Hybrid methods USING genetic algorithms for global optimization. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 26 2 (1996) 243-258
47. Reynolds R.G., and Zhu S. Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 31 1 (2001) 1-18
48. Righini G., and Trubian M. A worst-case analysis of two approximate algorithms for the asymmetric traveling salesman problem. European Journal of Operational Research 81 3 (1995) 553-556
49. Righini G., and Trubian M. A note on the approximation of the asymmetric traveling salesman problem. European Journal of Operational Research 153 1 (2004) 255-265
50. Ross B.J. A Lamarckian evolution strategy for genetic algorithms. In: Chambers L.D. (Ed). Practical Handbook of Genetic Algorithms: Complex Coding Systems vol. 3 (1999), CRC Press, Boca Raton, FL 1-16
51. Santosa C.A., Spimb J.A., and Garcia A. Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel. Engineering Applications of Artificial Intelligence 16 5-6 (2003) 511-527
52. Schmitt L., and Amini M.M. Performance characteristics of alternative genetic algorithmic approaches to the traveling salesman problem using path representation: an empirical study. European Journal of Operational Research 108 3 (1998) 551-570
53. Tan K.C., Lee L.H., and Ou K. Artificial intelligence heuristics in solving vehicle routing problems with time window constraints. Engineering Applications of Artificial Intelligence 14 6 (2001) 825-837
54. Vachhani P., Rengaswamy R., and Venkatasubramanian V. A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems. Chemical Engineering Science 56 6 (2001) 2133-2148
55. Waddington C.H. Canalization of development and the inheritance of acquired characteristics. Nature 150 3811 (1942) 563-565
56. Walters T. Repair and brood selection in the traveling salesman problem. Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature (1998), Springer, Amsterdam 813-822
57. Wang L. A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation 170 2 (2005) 1329-1343
58. Wang L., and Zheng D.Z. An effective hybrid optimization strategy for job-shop scheduling problems. Computers and Operations Research 28 6 (2001) 585-596
59. Wang H.-F., and Wu K.-Y. Hybrid genetic algorithm for optimization problems with permutation property. Computers and Operations Research 31 4 (2004) 2453-2471
60. Whitley D., Gordan V.S., and Mathias K. Lamarckian evolution, the Baldwin affects and function optimization. In: Davidor Y., Schwefel H.-P., and Manner R. (Eds). Lecture Notes in Computer Science vol. 866 (1994), Springer, Berlin 6-15
61. Yang S.M., Shao D.G., and Luo Y.J. A novel evolution strategy for multiobjective optimization problem. Applied Mathematics and Computation 170 2 (2005) 850-873
62. Zhang W.X. A note on the complexity of the asymmetric traveling salesman problem. Operations Research Letters 20 1 (1997) 31-38