Abstract
We are interested in improving the performance of genetic algorithm (GA) to solve a combinatirial optimization problem. Several approaches have been developed based on the adaptation and improvement of different standard genetic operators. However, GA also has some significant drawbacks, for instance, the premature convergence of computations, expensive computation from evolutional procedures, and the poor capability of local search. Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. In this paper, an immune based genetic algorithms are proposed to overcome these inconvenients for traveling salesman problem us a typical combinatirial optimization problem. Numerical results are presented for different standard instances from the TSPlib showing the performance of the proposed algorithms.
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References
Savla, K., Frazzoli, E., Bullo, F.: Traveling salesperson problems for the dubins vehicle. Autom. Contr. IEEE Trans. 53(6), 1378–1391 (2008)
Marinakis, Y., Marinaki, M., Dounias, G.: A hybrid particle swarm optimization algorithm for the vehicle routing problem. Eng. Appl. Artif. Intell. 23(4), 463–472 (2010)
Karaboga, D., Gorkemli, B.: A combinatorial artificial bee colony algorithm for traveling salesman problem. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 50–53 (2011)
Osaba, E., Yang, X.S., Diaz, F., Garcia, P., Carballedo, R.: An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng. Appl. Artif. Intell. 48, 59–71 (2016)
Holland, J.H.: Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, (1975). Goldberg, D.: Genetic Algorithm in Search, Optimization, and Machine Learning, Addison Wesley (1989)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley, Reading, MA (1989)
Abdoun, O., Tajani, C., Abouchabaka, J., El Khatir, H.: Improved genetic algorithm to solve asymmetric traveling salesman problem. Int. J. Open Problems Compt. Math. 9(4), 42–55 (2016)
Tajani, C., Abdoun, O., Lahjouji, A.I.: Genetic algorithm adopting immigration operator to solve the asymmetric traveling salesman problem. Int. J. Pure Appl. Math. 115(4), 801–812 (2017)
Jerne, N.K.: The immune system. Sci. Am. 229(1), 52–60 (1973)
De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Workshop Proceedings of the GECCO 2000, 36–37 (2000)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, New York (2002)
Gong, M., Jiao, L., Zhang, L.: Baldwinian learning in clonal selection algorithm for optimization. Inf. Sci. 180(8), 1218–1236 (2010)
Reinelt, G.: The TSPLIB symmetric traveling salesman problem instances. (1995) Available in: http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsp/index.htm
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Lahjouji El Idrissi, A., Tajani, C., Krkri, I., Fakhouri, H. (2019). Immune Based Genetic Algorithm to Solve a Combinatorial Optimization Problem: Application to Traveling Salesman Problem. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_82
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DOI: https://doi.org/10.1007/978-3-030-11928-7_82
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