A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP

Applied Soft Computing

Volumn 25, Issue , 2014, Pages 234-241

  Elloumi, Walid ^a   El Abed, Haikal ^a,b   Abraham, Ajith ^a,c   Alimi, Adel M ^a  

^a UNIVERSITY OF SFAX   (Tunisia)

^b German International Cooperation   (Saudi Arabia)

^c SCIENTIFIC NETWORK FOR INNOVATION AND RESEARCH EXCELLENCE   (United States)

Author keywords

Ant colony optimization; Multi objective optimization; Particle swarm optimization; Swarm intelligence; Traveling salesman problem

Indexed keywords

ANT COLONY OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION; SWARM INTELLIGENCE; TRAVELING SALESMAN PROBLEM; ALGORITHMS; ARTIFICIAL INTELLIGENCE; OPTIMIZATION; PROBLEM SOLVING; PARTICLE SWARM OPTIMIZATION (PSO);

ACO ALGORITHMS; ANT COLONY OPTIMIZATION ALGORITHMS; COMPARATIVE STUDIES; COMPLETION TIME; COMPLEX OPTIMIZATION PROBLEMS; CONVENTIONAL APPROACH; EFFECTIVENESS AND EFFICIENCIES; PSO ALGORITHMS; SWARM INTELLIGENCE;

PARTICLE SWARM OPTIMIZATION (PSO); ANT COLONY OPTIMIZATION;

EID: 84908142410     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.09.031     Document Type: Article

References (44)

1. E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence-from Natural to Artificial Systems, Oxford University Press, Oxford, 1999.
2. S. Camazine, J.L. Deneubourg, N.R. Franks, J. Sneyd, G. Theraulaz, E. Bonabeau, Self Organization in Biological Systems, Princeton University Press, Princeton, 2001.
3. M. Burd, Ecological consequences of traffic organisation in ant societies, Physica A: Stat. Theor. Phys. 372 (1) (2006) 124-131.
4. A. Dussutour, V. Fourcassi, D. Helbing, J.L. Deneubourg, Optimal traffic organization in ants under crowded conditions, Nature 428 (2004) 70-73.
5. K. Vittori, G. Talbot, J. Gautrais, V. Fourcassi, A.F. Araujo, G. Theraulaz, Path efficiency of ant foraging trails in an arti.cial network, J. Theor. Biol. 239 (2006) 507-515.
6. S.N. Beshers, J.H. Fewell, Models of division of labor in social insects, Annu. Rev. Entomol. 46 (1) (2001) 413-440.
7. C. Detrain, J.L. Deneubourg, Self-organized structures in a superorganism: do ants "behave" like molecules, Phys. Life Rev. 3 (3) (2006) 162-187.
8. E. Bonabeau, G. Theraulaz, J.L. Deneubourg, S. Aron, S. Camazine, Self-organization in social insects, Trends Ecol. Evol. 12 (5) (1997) 188-193.
9. G.K. Purushothama, L. Jenkins, Simulated annealing with local search: a hybrid algorithm for unit commitment, IEEE Trans. Power Syst. 18 (1) (2003) 273-278.
10. J. Kennedy, R.C. Eberhart, Particle swarm optimization, IEEE Int. Conf. Neural Netw. (1995) 1942-1948.
11. V. Miranda, Evolutionary algorithms with particle swarm movements., in: In IEEE 13th International Conference on Intelligent Systems Application to Power Systems, November, 2005, pp. 6-21.
12. W. Elloumi, Adel M. Alimi, Combinatory optimization of ACO and PSO, in: International Conference on Metaheuristique and Nature Inspired Computing, October, 2008, pp. 1-8.
13. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: optimization by a colony of cooperating agents., IEEE Trans. Syst. Man Cybern. B Cybern. 26 (February (2)) (1996) 29-41.
14. T.G. Crainic, M. Grendeau, Coperative parallel tabu search for capacitated network design, J. Heuristics 8 (2002) 601-627.
15. M. Nowostawski, R. Poli, Parallel genetic algorithms taxonomy, in: In Proc. 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems, 1999, pp. 88-92.
16. E. Cantu-Paz, A Survey PF Parallel Genetic Algorithms Technical Report Illegal 97003, The University of Illinois, 1997.
17. M. Middendorf, F. Reischle, H. Schmeck, Multi colony ant algorithms, J. Heuristics 8 (2002) 305-320.
18. S. Baskar, P.N. Suganthan, A novel current particle swarm optimization, Proc. IEEE Congr. Evol. Comput. 1 (2004) 792-796.
19. D.G. Cabrero, C. Armero, D.N. Ranasinghe, The travelling salesman's problem: a self-adapting PSO-ACS algorithm, in: International Conference on Industrial and Information Systems, 2007.
20. E. Talbi, A taxonomy of hybrid metaheuristics, J. Heuristics 8 (2002) 541-564.
21. M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents., IEEE Trans. Syst. Man Cybern. (1996) 1-13.
22. A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by ant colonies, in: I. n. F. Varela, P. Bourgine (Eds.), Proceedings of ECAL'91 - First European Conference on Artificial Life, Elsevier Publishing, Paris, France, 1992, pp. 134-142.
23. M. Dorigo, L.M. Gambardella, Ant colony system: a cooperative learning approach to the travelling salesman problem, IEEE Trans. Evol. Comput. (1997).
24. J. Kennedy, R. Mendez, Population structure and particle swarm performance, in: Proc. IEEE. Congress on Coevolutionary Computation, vol. 2, 2002, pp. 1671-1676.
25. R.C. Eberhart, P. Simpson, R. Dobbins, Computational intelligence, in: PC Tools: Academic Chapter 6, 1996, pp. 212-226.
26. Y. Yang, M. Kamel, Clustering ensemble using swarm intelligence, in: Proc. IEEE Swarm Intelligence Symposium, 2003, pp. 65-71.
27. W. Elloumi, N. Rokbani, Adel M. Alimi, Ant Supervised By PSO, in: International Symposium on Computational Intelligence and Intelligent Informatics, October, 2009, pp. 161-166.
28. W. Elloumi, Adel M. Alimi, A More Efficient MOPSO for Optimization, in: The eight ACS/IEEE International Conference on Computer Systems and Applications, May, 2010, pp. 1-7.
29. Z. Michalewicz, D.B. Fogel, How to Solve It: Modern Heuristics, Springer, Berlin, New York, 2004.
30. D.T. Pham, D. Karaboga, Intelligent Optimisation Techniques, Springer, London, New York, 2000.
31. J.M. Zurada, Introduction to Artificial Neural Systems, West, St. Paul, 1992.
32. V. Ramos, C. Fernandes, A.C. Rosa, Social cognitive maps, swarm collective perception and distributed search on dynamic landscapes, J. New Media Neural Cogn. Sci., NRW, Germany (2005), for his inaugural issue.
33. S. Garnier, J. Gautrais, G. Theraulaz, The biological principles of swarm intelligence, in: Swarm Intelligence, 2007.
34. A.J. Ouyang, Y.Q. Zhou, An improved PSO-ACO algorithm for solving large-scale TSP, J. Adv. Mater. Res. 1 (2010) 1154-1158.
35. S. Nonsiri, S. Supratid, Modifying ant colony optimization, in: IEEE Conference on Soft Computing in Industrial Applications, 2008.
36. F. Van den Bergh, A.P. Engelbrecht, A co-operative approach to particle swarm optimization, IEEE Trans. Evol. Comput. 8 (3) (2004) 225-239.
37. W. Elloumi, N. Baklouti, A. Abraham, Adel M. Alimi, Hybridization of fuzzy PSO and fuzzy ACO applied to TSP, in: 13th International Conference on Hybrid Intelligent Systems (HIS), December, 2013, pp. 106-111.
38. K. Xu, M. Jiang, D. Yuan, Parallel artificial bee colony algorithm for the traveling salesman problem, in: Proceedings of the 2nd International Conference on Computer and Information Application (ICCIA 2012), 2012, pp. 663-667.
39. L.P. Wong, M. Low, C.S. Chong, Bee colony optimization with local search for traveling salesman problem, in: Proceedings of the 6th IEEE International Conference on Industrial Informatics (INDIN 2008), 2008, pp. 1019-1025.
40. S. Liu, A powerful genetic algorithm for traveling salesman problem, in: Proceedings of the course Principles of Artificial Intelligence, 2012.
41. Z.H. Ahmed, Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator, Int. J. Biometrics & Bioinform. 3 (6) (2010) 96-105.
42. W. Elloumi, N. Baklouti, A. Abraham, Adel M. Alimi, The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization, J. Intell. Fuzzy Syst. (2014), http://dx.doi.org/10.3233/IFS-131020, Netherlands.
43. J.Q. Yang, J.G. Yang, A novel genetic algorithm for traveling salesman problem based on neighborhood code, in: Second International Conference on Intelligent Networks and Intelligent Systems, 2009, pp. 429-432.
44. Y.H. Chang, C.W. Chang, C.W. Tao, H.W. Lin, J.S. Taur, Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization, Expert Syst. Appl. 39 (2012) 3624-3633.