A multi-objective chemical reaction optimisation algorithm for multi-objective travelling salesman problem

International Journal of Innovative Computing and Applications

Volumn 6, Issue 2, 2014, Pages 87-101

  Bouzoubia, Samira ^a   Layeb, Abdesslem ^a   Chikhi, Salim ^a  

^a University Constantine 2   (Algeria)

Author keywords

Bio inspired algorithm; CRO; MOTSP; Multi objective chemical reaction; Multi objective optimisation; Multi objective travelling salesman problem

Indexed keywords

BIOMIMETICS; CATHODE RAY OSCILLOSCOPES; CHEMICAL REACTIONS; COMBINATORIAL OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION; NP-HARD;

BIO-INSPIRED ALGORITHMS; COMPARATIVE STUDIES; COMPUTING THEORY; MOTSP; MULTI OBJECTIVE; NON-DOMINATED SORTING; REAL-LIFE APPLICATIONS; TRAVELLING SALESMAN PROBLEM;

TRAVELING SALESMAN PROBLEM;

EID: 84920406709     PISSN: 1751648X     EISSN: 17516498     Source Type: Journal    
DOI: 10.1504/IJICA.2014.066498     Document Type: Article

References (41)

1. Angel, E., Bampis, E. and Gourves, L. (2004) A dynasearch neighborhood for the bicriteria traveling salesman problem, in Gandibleux, X., Sevaux, M., Sorensen, K. and Tkindt, V. (Eds.): Metaheuristics for Multiobjective Optimisation, LNEMS, Vol. 535, pp.153-176, Springer, Berlin.
2. Angus, D. (2007) Crowding population-based ant colony optimisation for the multi-objective travelling salesman problem, IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp.333-340.
3. Bandyopadhyay, S., Saha, S., Maulik, U. and Deb, K. (2008) A simulated annealing based multi-objective optimization algorithm: A MOSA, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 3, pp.269-283.
4. Coello, C.A.C., Van Veldhuizen, D.A. and Lamont, G.B. (2002) Evolutionary Algorithms for Solving Multi-Objective Problems, Vol. 242, Kluwer Academic, New York.
5. Collette, Y. and Siarry, P. (2003) Multi-objective Optimization Principals and Case Studies, Springer-Verlag, Berlin, Heidelberg, New York, ISBN: 3-540-40182-2.
6. Deb, K., Agrawal, S., Pratab, A. and Meyarivan, T. (2000) A Fast Elitist Non-dominated Sorting Genetic Algorithms for Multi-objective Optimization: NSGA-II Kangal Report 200001, Tech. rep., Indian Institute of Technology, Kanpur, India.
7. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp.182-197.
8. Dorigo, M. and Stutzle, T. (2004) Ant Colony Optimization, The MIT Press Cambridge, Massachusetts London, England.
9. Elaoud, S., Teghem, J. and Loukil, T. (2010) Multiple crossover genetic algorithm for the multiobjective traveling salesman problem, International Symposium on Combinatorial Optimization, pp.939-946.
10. Fischer, R. and Richter, K. (1982) Solving a multiobjective traveling salesman problem by dynamic programming, Optimization, Vol. 13, No. 2, pp.247-252.
11. Gao, F., Zhou, A. and Zhang, G. (2012) An estimation of distribution algorithm based on decomposition for the multi-objective TSP, in International Conference on Natural Computation (ICNC 2012), pp.817-821.
12. Garcia-Martinez, C., Cordon, O. and Herrera, F. (2007) A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP, European Journal of Operational Research, Vol. 180, No. 1, pp.116-148.
13. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, USA.
14. Guggenheim, E. (1967) Thermodynamics: An Advanced Treatment for Chemists and Physicists, 5th ed., North Holland, John Wiley & Sons (Interscience), Library of Congress Catalog No. 67-20003.
15. Iredi, S., Merkle, D. and Middendorf, M. (2001) Bi-criterion optimization with multi colony ant algorithms, Evolutionary Multi-Criterion Optimization, pp.359-372, Springer.
16. Jozefowiez, N., Glover, F. and Laguna, M. (2008) Multi-objective meta-heuristics for the traveling salesman problem with profits, Journal of Mathematical Modelling and Algorithms, Vol. 7, No. 2, pp.177-195.
17. Ke, L., Zhang, Q. and Battiti, R. (2013) MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and ant colony, IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp.1845-1859.
18. Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983) Optimization by simulated annealing, Science, Vol. 220, No. 4598, pp.671-680.
19. Knowles, J. and Corne, D. (2000) Approximating the nondominated front using the Pareto archived evolution strategy, Evolutionary Computation, Vol. 8, No. 2, pp.149-172.
20. Kumar, R. and Singh, P.K. (2007) Pareto evolutionary algorithm hybridized with local search for biobjective TSP, in Grosan, C., Abraham, A. and Ishibuchi, H. (Eds.): Hybrid Evolutionary Algorithms, Ch. 14, Springer, Heidelberg.
21. Lam, A. and Li, V. (2010) Chemical-reaction-inspired meta-heuristic for optimization, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp.381-399.
22. Lam, A. and Li, V. (2012) Chemical reaction optimization: A tutorial, Memetic Comp., pp.3-17, Springer, DOI: 10.1007/s12293-012-0075-1.
23. Li, H. and Landa-Silva, D. (2008) Evolutionary multi-objective simulated annealing with adaptive and competitive search direction, IEEE Congress on Evolutionary Computation, pp.3312-3318.
24. Li, J-Q. and Pan, Q-K. (2012) Chemical reaction optimization for flexible job-shop scheduling problems with maintenance activity, Applied Soft Computing, Vol. 12, No. 9, pp.2896-2912.
25. Lust, T. and Teghem, J. (2010) The multi-objective traveling sales-man problem: A survey and a new approach, Nature Inspired Computing SCI, Vol. 272, pp.119-137, Springer-Verlag, Berlin, Heidelberg.
26. Marti, R. and Reinelt, G. (2011) The Linear Ordering Problem, Exact and Heuristic Methods in Combinatorial Optimization, Springer, Heidelberg, Dordrecht, London, New York.
27. Miettinen, K. (1999) Nonlinear Multi-objective Optimization, Kluwer, Norwell, MA.
28. Paquete, L., Chiarandini, M. and Stutzle, T. (2004) Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study, in Gandibleux, X., Sevaux, M., Sorensen, K. and Tkindt, V. (Eds.): Metaheuristics for Multiobjective Optimisation, LNEMS, Vol. 535, pp.177-199, Springer, Berlin, Heidelberg.
29. Poonam, G. (2009) A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm, International Journal of Network Security & Its Applications, Vol. 1, No. 1, pp.34-42.
30. Reinelt, G. (1991) TSPLIB - A traveling salesman problem library, ORSA Journal on Computing, Vol. 3, No. 4, pp.376-384.
31. Rosenkrantz, D.J., Stearns, R.E. and Lewis, M.P. (1977) An analysis of several heuristics for the traveling salesman problem, SIAM Journal on Computing, Vol. 6, No. 3, pp.563-581.
32. Sun, J., Wang, Y., Li, J. and Gao, K. (2011) Hybrid algorithm based on chemical reaction optimization and Lin-Kernighan local search for the traveling salesman problem, in Seventh International Conference on Natural Computation, pp.1518-1521.
33. Tan, K., Khor, E. and Lee, T. (2005) Multi-objective evolutionary algorithms and applications, Advanced Information and Knowledge Processing, Springer-Verlag, Berlin, Germany.
34. Truong, T-K., Li, K. and Xua, Y. (2013) Chemical reaction optimization with greedy strategy for the 0-1 knapsack problem, Applied Soft Computing, Vol. 13, No. 4, pp.1774-1780.
35. Truong, T-K., Salah, A., Xu, Y. and Fan, S. (2014) A Parallel Chemical Reaction Optimization for Multiple Choice Knapsack Problem, pp.482-489, Springer-Verlag, Berlin, Heidelberg.
36. Xu, Y., Li, K., He, L. and Truong, K-T. (2013) A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization, J. Parallel Distrib. Comput., Vol. 73, No. 9, pp.1306-1322.
37. Yan, Z., Zhang, L., Kang, L. and Lin, G. (2003) A new MOEA for multi-objective tsp and its convergence property analysis, in Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K. and Thiele, L. (Eds.): EMO 2003, LNCS, p.2632, Springer, Heidelberg.
38. Yang, X-S. and Deb, S. (2009) Cuckoo search via levy flights, World Congress on Nature Biologically Inspired Computing (NaBIC 2009), India, IEEE Publications, USA, pp.210-214.
39. Zhang, Q. and Li, H. (2007) MOEA/D: A multi-objective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, Vol. 11, No. 6, pp.712-731.
40. Zhong, Y., Liu, X., Wang, L. and Wang, C. (2012) Particle swarm optimisation algorithm with iterative improvement strategy for multi-dimensional function optimisation problems, Int. J. of Innovative Computing and Applications, Vol. 4, Nos. 3/4, pp.223-232.
41. Zitzler, E. (1999) Evolutionary Algorithms for Multi-Objective Optimization: Methods and Applications, PhD thesis, Swiss Federal Institute of Technology, Zurich.