How to cite this paper
Smaili, F. (2024). A hybrid genetic-simulated annealing algorithm for multiple traveling salesman problems.Decision Science Letters , 13(3), 709-728.
Refrences
Albayrak, M., & Allahverdi, N. (2011). Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications, 38(3), 1313-1320.
Al-Omeer, M. A., & Ahmed, Z. H. (2019, April). Comparative study of crossover operators for the MTSP. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
Altenberg, L. (1995). The schema theorem and Price's theorem. In Foundations of genetic algorithms (Vol. 3, pp. 23-49). Elsevier.
Ann, S., Kim, Y., & Ahn, J. (2015). Area allocation algorithm for multiple UAVs area coverage based on clustering and graph method. IFAC-Papers OnLine, 48(9), 204-209.
Bagchi, P., & Pal, S. (2011, April). Controlling crossover probability in case of a genetic algorithm. In International Conference on Advances in Information Technology and Mobile Communication (pp. 287-290). Berlin, Heidelberg: Springer Berlin Heidelberg.
Banzhaf, W. (1990). The “molecular” traveling salesman. Biological Cybernetics, 64(1), 7-14.
Beed, R. S., Sarkar, S., Roy, A., & Chatterjee, S. (2017, December). A study of the genetic algorithm parameters for solving multi-objective travelling salesman problem. In 2017 International conference on information technology (ICIT) (pp. 23-29). IEEE.
Bodenhofer, U. (2004). Genetic algorithms: theory and applications.
Bremermann, H. J., Rogson, M., & Salaff, S. (1965). Search by evolution. Biophysics and Cybernetic Systems, 157-167.
Brown, E. C., Ragsdale, C. T., & Carter, A. E. (2007). A grouping genetic algorithm for the multiple traveling salesperson problem. International Journal of Information Technology & Decision Making, 6(02), 333-347.
Carter, A. E., & Ragsdale, C. T. (2006). A new approach to solving the multiple traveling salesperson problem using genetic algorithms. European journal of operational research, 175(1), 246-257.
Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of optimization theory and applications, 45, 41-51.
Deepa, S. N., & Sivanandam, S. N. (2010). Introduction to genetic algorithms. Springer.
Eglese, R. W. (1990). Simulated annealing: a tool for operational research. European journal of operational research, 46(3), 271-281.
Eiben, Á. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on evolutionary computation, 3(2), 124-141.
Eshelman, L. J. (1997). Crossover operator biases: Exploiting the population distribution. In Proceedings of the 7th International Conference on Genetic Algorithms (pp. 354-361).
Filip, E., & Otakar, M. (2011). The travelling salesman problem and its application in logistic practice. WSEAS Transactions on Business and Economics, 8(4), 163-173.
García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.
Grefenstette, J. J. (Ed.). (2013). Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms. Psychology Press.
Harrath, Y., Salman, A. F., Alqaddoumi, A., Hasan, H., & Radhi, A. (2019). A novel hybrid approach for solving the multiple traveling salesmen problem. Arab Journal of Basic and applied sciences, 26(1), 103-112.
Holland, J. H. (1975). Adption in natural and artifieial system.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Labadie, N., Melechovsky, J., & Prins, C. (2014). An evolutionary algorithm with path relinking for a bi-objective multiple traveling salesman problem with profits. Applications of Multi-Criteria and Game Theory Approaches: Manufacturing and Logistics, 195-223.
Laporte, G. (1992). The traveling salesman problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(2), 231-247.
Laporte, G., & Nobert, Y. (1980). A cutting planes algorithm for the m-salesmen problem. Journal of the Operational Research society, 31, 1017-1023.
Li, J., Sun, Q., Zhou, M., & Dai, X. (2013, October). A new multiple traveling salesman problem and its genetic algorithm-based solution. In 2013 IEEE international conference on systems, man, and cybernetics (pp. 627-632). IEEE.
Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations research, 21(2), 498-516.
Lo, K. M., Yi, W. Y., Wong, P. K., Leung, K. S., Leung, Y., & Mak, S. T. (2018). A genetic algorithm with new local operators for multiple traveling salesman problems. International Journal of Computational Intelligence Systems, 11(1), 692-705.
Malmborg, C. J. (1996). A genetic algorithm for service level based vehicle scheduling. European journal of operational research, 93(1), 121-134.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Ming, L. I. U., & Pei-yong, Z. H. A. N. G. (2014). New hybrid genetic algorithm for solving the multiple traveling salesman problem: an example of distribution of emergence materials. Journal of Systems & Management, 23(2), 247.
Mooi, L. S. (2016). Crossover and mutation operators of real coded genetic algorithms for global optimization problems.
Necula, R., Breaban, M., & Raschip, M. (2015, November). Tackling the bi-criteria facet of multiple traveling salesman problem with ant colony systems. In 2015 IEEE 27th international conference on tools with artificial intelligence (ICTAI) (pp. 873-880). IEEE.
Qiang, N., & Kang, F. J. (2014). A hybrid particle swarm optimization for solving vehicle routing problem with stochastic demands. Advanced Materials Research, 971, 1467-1472.
Reinelt, G. (2003). The traveling salesman: computational solutions for TSP applications (Vol. 840). Springer.
Shim, V. A., Tan, K. C., & Tan, K. K. (2012, June). A hybrid estimation of distribution algorithm for solving the multi-objective multiple traveling salesman problem. In 2012 IEEE congress on evolutionary computation (pp. 1-8). IEEE.
Shmoys, D. B., Lenstra, J. K., Kan, A. R., & Lawler, E. L. (Eds.). (1985). The traveling salesman problem (Vol. 12). John Wiley & Sons, Incorporated.
Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108-116.
Singh, D. R., Singh, M. K., Singh, T., & Prasad, R. (2018). Genetic algorithm for solving multiple traveling salesmen problem using a new crossover and population generation. Computación y Sistemas, 22(2), 491-503.
Soylu, B. (2015). A general variable neighborhood search heuristic for multiple traveling salesmen problem. Computers & Industrial Engineering, 90, 390-401.
Tang, L., Liu, J., Rong, A., & Yang, Z. (2000). A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. European Journal of Operational Research, 124(2), 267-282.
Tang, P. H., & Tseng, M. H. (2013). Adaptive directed mutation for real-coded genetic algorithms. Applied Soft Computing, 13(1), 600-614.
Wong, D. F., & Liu, C. L. (1986, June). A new algorithm for floorplan design. In 23rd ACM/IEEE Design Automation Conference (pp. 101-107). IEEE.
Xu, X., Yuan, H., Liptrott, M., & Trovati, M. (2018). Two phase heuristic algorithm for the multiple-travelling salesman problem. Soft Computing, 22, 6567-6581.
Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Modification of the ant colony optimization for solving the multiple traveling salesman problem. Romanian Journal of Information Science and Technology, 16(1), 65-80.
Yu, Z., Jinhai, L., Guochang, G., Rubo, Z., & Haiyan, Y. (2002, June). An implementation of evolutionary computation for path planning of cooperative mobile robots. In Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No. 02EX527) (Vol. 3, pp. 1798-1802). IEEE.
Al-Omeer, M. A., & Ahmed, Z. H. (2019, April). Comparative study of crossover operators for the MTSP. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
Altenberg, L. (1995). The schema theorem and Price's theorem. In Foundations of genetic algorithms (Vol. 3, pp. 23-49). Elsevier.
Ann, S., Kim, Y., & Ahn, J. (2015). Area allocation algorithm for multiple UAVs area coverage based on clustering and graph method. IFAC-Papers OnLine, 48(9), 204-209.
Bagchi, P., & Pal, S. (2011, April). Controlling crossover probability in case of a genetic algorithm. In International Conference on Advances in Information Technology and Mobile Communication (pp. 287-290). Berlin, Heidelberg: Springer Berlin Heidelberg.
Banzhaf, W. (1990). The “molecular” traveling salesman. Biological Cybernetics, 64(1), 7-14.
Beed, R. S., Sarkar, S., Roy, A., & Chatterjee, S. (2017, December). A study of the genetic algorithm parameters for solving multi-objective travelling salesman problem. In 2017 International conference on information technology (ICIT) (pp. 23-29). IEEE.
Bodenhofer, U. (2004). Genetic algorithms: theory and applications.
Bremermann, H. J., Rogson, M., & Salaff, S. (1965). Search by evolution. Biophysics and Cybernetic Systems, 157-167.
Brown, E. C., Ragsdale, C. T., & Carter, A. E. (2007). A grouping genetic algorithm for the multiple traveling salesperson problem. International Journal of Information Technology & Decision Making, 6(02), 333-347.
Carter, A. E., & Ragsdale, C. T. (2006). A new approach to solving the multiple traveling salesperson problem using genetic algorithms. European journal of operational research, 175(1), 246-257.
Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of optimization theory and applications, 45, 41-51.
Deepa, S. N., & Sivanandam, S. N. (2010). Introduction to genetic algorithms. Springer.
Eglese, R. W. (1990). Simulated annealing: a tool for operational research. European journal of operational research, 46(3), 271-281.
Eiben, Á. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on evolutionary computation, 3(2), 124-141.
Eshelman, L. J. (1997). Crossover operator biases: Exploiting the population distribution. In Proceedings of the 7th International Conference on Genetic Algorithms (pp. 354-361).
Filip, E., & Otakar, M. (2011). The travelling salesman problem and its application in logistic practice. WSEAS Transactions on Business and Economics, 8(4), 163-173.
García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.
Grefenstette, J. J. (Ed.). (2013). Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms. Psychology Press.
Harrath, Y., Salman, A. F., Alqaddoumi, A., Hasan, H., & Radhi, A. (2019). A novel hybrid approach for solving the multiple traveling salesmen problem. Arab Journal of Basic and applied sciences, 26(1), 103-112.
Holland, J. H. (1975). Adption in natural and artifieial system.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Labadie, N., Melechovsky, J., & Prins, C. (2014). An evolutionary algorithm with path relinking for a bi-objective multiple traveling salesman problem with profits. Applications of Multi-Criteria and Game Theory Approaches: Manufacturing and Logistics, 195-223.
Laporte, G. (1992). The traveling salesman problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(2), 231-247.
Laporte, G., & Nobert, Y. (1980). A cutting planes algorithm for the m-salesmen problem. Journal of the Operational Research society, 31, 1017-1023.
Li, J., Sun, Q., Zhou, M., & Dai, X. (2013, October). A new multiple traveling salesman problem and its genetic algorithm-based solution. In 2013 IEEE international conference on systems, man, and cybernetics (pp. 627-632). IEEE.
Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations research, 21(2), 498-516.
Lo, K. M., Yi, W. Y., Wong, P. K., Leung, K. S., Leung, Y., & Mak, S. T. (2018). A genetic algorithm with new local operators for multiple traveling salesman problems. International Journal of Computational Intelligence Systems, 11(1), 692-705.
Malmborg, C. J. (1996). A genetic algorithm for service level based vehicle scheduling. European journal of operational research, 93(1), 121-134.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Ming, L. I. U., & Pei-yong, Z. H. A. N. G. (2014). New hybrid genetic algorithm for solving the multiple traveling salesman problem: an example of distribution of emergence materials. Journal of Systems & Management, 23(2), 247.
Mooi, L. S. (2016). Crossover and mutation operators of real coded genetic algorithms for global optimization problems.
Necula, R., Breaban, M., & Raschip, M. (2015, November). Tackling the bi-criteria facet of multiple traveling salesman problem with ant colony systems. In 2015 IEEE 27th international conference on tools with artificial intelligence (ICTAI) (pp. 873-880). IEEE.
Qiang, N., & Kang, F. J. (2014). A hybrid particle swarm optimization for solving vehicle routing problem with stochastic demands. Advanced Materials Research, 971, 1467-1472.
Reinelt, G. (2003). The traveling salesman: computational solutions for TSP applications (Vol. 840). Springer.
Shim, V. A., Tan, K. C., & Tan, K. K. (2012, June). A hybrid estimation of distribution algorithm for solving the multi-objective multiple traveling salesman problem. In 2012 IEEE congress on evolutionary computation (pp. 1-8). IEEE.
Shmoys, D. B., Lenstra, J. K., Kan, A. R., & Lawler, E. L. (Eds.). (1985). The traveling salesman problem (Vol. 12). John Wiley & Sons, Incorporated.
Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108-116.
Singh, D. R., Singh, M. K., Singh, T., & Prasad, R. (2018). Genetic algorithm for solving multiple traveling salesmen problem using a new crossover and population generation. Computación y Sistemas, 22(2), 491-503.
Soylu, B. (2015). A general variable neighborhood search heuristic for multiple traveling salesmen problem. Computers & Industrial Engineering, 90, 390-401.
Tang, L., Liu, J., Rong, A., & Yang, Z. (2000). A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. European Journal of Operational Research, 124(2), 267-282.
Tang, P. H., & Tseng, M. H. (2013). Adaptive directed mutation for real-coded genetic algorithms. Applied Soft Computing, 13(1), 600-614.
Wong, D. F., & Liu, C. L. (1986, June). A new algorithm for floorplan design. In 23rd ACM/IEEE Design Automation Conference (pp. 101-107). IEEE.
Xu, X., Yuan, H., Liptrott, M., & Trovati, M. (2018). Two phase heuristic algorithm for the multiple-travelling salesman problem. Soft Computing, 22, 6567-6581.
Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Modification of the ant colony optimization for solving the multiple traveling salesman problem. Romanian Journal of Information Science and Technology, 16(1), 65-80.
Yu, Z., Jinhai, L., Guochang, G., Rubo, Z., & Haiyan, Y. (2002, June). An implementation of evolutionary computation for path planning of cooperative mobile robots. In Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No. 02EX527) (Vol. 3, pp. 1798-1802). IEEE.