Use whale algorithm and neighborhood search metaheuristics with fuzzy values to solve the location problem
Subject Areas : Operation Research
Mehdi Fazli
1
,
Farzin Modarres Khiabani
2
,
Behrooz Daneshian
3
1 - Department of Mathematics, Islamic Azad University, Tabriz Branch, Tabriz, Iran
2 - Department of Mathematics, Islamic Azad University, Tabriz Branch, Tabriz, Iran
3 - Department of Mathematics, Islamic Azad University, Central Tehran Branch, Tehran, Iran
Keywords: Location problem, Meta Heuristic, fuzzy function, Whale algorithm, Neighborhood search algorithm,
Abstract :
In this paper, a facility location model with fuzzy value parameters based on the meta-heuristic method is investigated and solved. The proposed method and model uses fuzzy values to investigate and solve the problem of location allocation. The hypotheses of the problem in question are considered as fuzzy random variables and the capacity of each facility is assumed to be unlimited. This article covers a modern, nature-inspired method called the whale algorithm and the neighborhood search method. The proposed method and related algorithm are tested with practical optimization problems and modeling problems. To evaluate the efficiency and performance of the proposed method, we apply this method to our location models in which fuzzy coefficients are used. The results of numerical optimization show that the proposed method performs better than conventional methods.
Alatas, B. (2011). ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization. Expert Syst. Appl., 38(10), 13170–13180. doi:10.1016/j.eswa.2011.04.126
Alba, E., Luque, G., & Troya, J. M. (2004). Parallel LAN/WAN heuristics for optimization. Parallel Computing, 30(5-6), 611-628.
Albareda-Sambola, M., Fernández, E., & Nickel, S. (2012). Multiperiod location-routing with decoupled time scales. European journal of operational research, 217(2), 248-258.
Ambrosino, D., & Scutella, M. G. (2005). Distribution network design: New problems and related models. European journal of operational research, 165(3), 610-624.
Chiu, Y. C., Chang, L. C., & Chang, F. J. (2007). Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation. Hydrological Processes, 21(23), 3162-3172.
Drezner, Z. (2008). Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Computers & Operations Research, 35(3), 717-736.
Du, H., Wu, X., & Zhuang, J. (2006). Small-world optimization algorithm for function optimization. Paper presented at the International Conference on Natural Computation.
Erol, O. K., & Eksin, I. (2006). A new optimization method: big bang–big crunch. Advances in Engineering Software, 37(2), 106-111.
Formato, R. (2007). Central force optimization: A new metaheuristic with applications in applied electromagnetics. Progress in electromagnetics research. PIER 77, 425–491. In.
Gao, J., Sun, L., & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research, 35(9), 2892-2907. doi:https://doi.org/10.1016/j.cor.2007.01.001
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning: Addison-Wesley Longman Publishing Co., Inc.
Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., Mckenna, M. F., Simon, M., & Nowacek, D. P. (2013). Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology. BioScience, 63(2), 90-100.
Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, 175-184.
Jaszkiewicz, A., & Kominek, P. (2003). Genetic local search with distance preserving recombination operator for a vehicle routing problem. European journal of operational research, 151(2), 352-364.
Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: ray optimization. Computers & structures, 112, 283-294.
Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3-4), 267-289.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Koza, J. R. (1992). Genetic Programming II, Automatic Discovery of Reusable Subprograms: MIT Press, Cambridge, MA.
Laporte, G., & Dejax, P. J. (1989). Dynamic location-routeing problems. Journal of the Operational Research Society, 40(5), 471-482.
Lee, Z.-J., Su, S.-F., Chuang, C.-C., & Liu, K.-H. (2008). Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Applied Soft Computing, 8(1), 55-78.
Moghaddam, F. F., Moghaddam, R. F., & Cheriet, M. (2012). Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
Salhi, S., & Nagy, G. (1999). Consistency and robustness in location-routing. Studies in Locational Analysis(13), 3-19.
Shah-Hosseini, H. (2011). Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. International Journal of Computational Science and Engineering, 6(1-2), 132-140.
Simon, D. (2008). Biogeography-Based Optimization. Trans. Evol. Comp, 12(6), 702–713. doi:10.1109/tevc.2008.919004
Tavakkoli-Moghaddam, R., Safaei, N., & Sassani, F. (2009). A memetic algorithm for the flexible flow line scheduling problem with processor blocking. Computers & Operations Research, 36(2), 402-414.
Watkins, W. A., & Schevill, W. E. (1979). Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus. Journal of Mammalogy, 60(1), 155-163.
Webster, B., & Bernhard, P. J. (2003). A local search optimization algorithm based on natural principles of gravitation. Retrieved from.