A novel metaheuristic optimization algorithm: the monarchy metaheuristic

  In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.

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  • Davis L. Applying adaptive algorithms to epistatic domains. In: IJCAI-85 the 9th International Joint Conference on Artificial Intelligence; 18–23 August 1985; California, USA. San Francisco, CA, USA: Morgan Kaufmann Publishers. pp. 162-164.
  • Croes GA. A method for solving traveling salesman problems. Oper Res 1958; 6: 791-812.
  • Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983; 220: 671-680.
  • Osaba E, Onieva E, Carballedo R, Diaz F, Perallos A. An adaptive multi-crossover population algorithm for solving routing problems. In: Terrazas G, Otero FB, Masegosa AD, editors. Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Cham, Switzerland: Springer, 2014. pp. 113-124.
  • Lin B, Sun XY, Salous S. Solving traveling salesman problem with an improved hybrid genetic algorithm. J Comput Commun 2016; 4: 98-106.
  • Wang X, Xu G. Hybrid differential evolution algorithm for traveling salesman problem. Procedia Eng 2011; 15: 2716-2720.
  • Bao H. A two phase hybrid optimization algorithm for solving complex optimization problems. Int J Smart Home 2015; 9: 27-36.
  • Gündüz M, Kiran MS, Özceylan E. A hierarchic approach based on swarm intelligence to solve the traveling salesman problem. Turk J Electr Eng Co 2015; 23: 103-117.
  • Osaba E, Díaz F. Comparison of a memetic algorithm and a tabu search algorithm for the traveling salesman problem. In: Computer Science and Information Systems (FedCSIS) 2012 Federated Conference on; 9–12 September 2012; Wrocław, Poland. New Jersey, USA: IEEE. pp. 131-136.
  • Gao S, Wang W, Dai H, Li F, Tang Z. Improved clonal selection algorithm combined with ant colony optimization. Ieice T Inf Syst 2008; 91: 1813-1823.
  • Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. Acm Comput Surv 2003; 35: 268-308.
  • Jacobson SH, Henderson D, Johnson AW. The theory and practice of simulated annealing. In: Glover F, Kochen- berger GA, editors. Handbook of Metaheuristics. Dordrecht, SH, Netherlands: Kluwer Academic Publishers: Springer, 2003. pp. 287-319.
  • Johnson DS, Aragon CR, McGeoch LA, Schevon C. Optimization by simulated annealing: an experimental evalu- ation part 1: graph partitioning. Oper Res 1991; 39: 378-406.
  • Blum C. Hybrid metaheuristics in combinatorial optimization: a tutorial. In: International Conference on Theory and Practice of Natural Computing; 2–4 October 2012; Tarragona, Spain. Berlin, Heidelberg, Germany: Springer. pp. 1-10.
  • Blum C, Puchinger J, Raidl GR, Roli A. Hybrid metaheuristics in combinatorial optimization: A survey. Appl Soft Comput 2011; 11: 4135-4151.
  • Ting O, Yang XS, Cheng S, Huang KZ. Hybrid metaheuristic algorithms: past, present, and future. In: Yang XS, editor. Recent Advances in Swarm Intelligence and Evolutionary Computation. Cham, Switzerland: Springer, 2015. pp. 71-83.
  • Hanif H, Idris I. Tree physiology optimization in constrained optimization problem. Telkomnika 2018; 16: 876-882.
  • Potvin JY. Genetic algorithms for the traveling salesman problem. Ann Oper Res 1996; 63: 337-370.
  • Blum C, Roli A. Hybrid metaheuristics: an introduction. In: Blum C, Aguilera MJB, Roli A, Sampels M, editors. Hybrid Metaheuristics. Berlin, Heidelberg, Germany: Springer, 2008. pp. 1-30.
  • Applegate LD, Bixby RE, Chvatal V, Cook WJ. The traveling salesman problem. Princeton, NJ, USA: Princeton University Press, 2006.
  • Siarry P. Metaheuristics. Cham, Switzerland: Springer, 2016.
  • Goldberg DE. Genetic algorithms in search optimization and machine learning. Boston, MA, USA: Addison-Wesley Publishing Company, 1989.
  • Glover FW, Kochenberger GA. Handbook of Metaheuristics. Dordrecht, SH, Netherlands: Kluwer Academic Pub- lishers, 2003.
  • Siarry P, Idoumghar L, Lepagnot J. Swarm Intelligence Based Optimization. Cham, Switzerland: Springer, 2016.
  • Sivanandam SN, Deepa SN. Introduction to Genetic Algorithms. Berlin, Heidelberg, Germany: Springer, 2008.
  • Jacobson L, Kanber B. Genetic Algorithms in Java Basics. New York, NY, USA: Apress, 2015.
  • Ghanea-Hercock R. Applied Evolutionary Algorithms in Java. New York, NY, USA: Springer, 2013.
  • Krzanowski RM, Raper J. Spatial Evolutionary Modeling. New York, NY, USA: Oxford University Press, 2001.
  • Blum C, Raidl GR. Hybrid Metaheuristics Powerful Tools for Optimization. Cham, Switzerland: Springer, 2016.
  • Gendreau M, Potvin JY. Handbook of Metaheuristics. New York, NY, USA: Springer, 2010.
  • Siarry P, Michalewicz Z. Advances in Metaheuristics for Hard Optimization. Berlin, Heidelberg, Germany: Springer, 2008.
  • Edelkamp S, Schrodl S. Heuristic Search Theory and Applications. Waltham, MA, USA : Elsevier, 2011.
  • Wang SC. Interdisciplinary Computing in Java Programming. New York, NY, USA: Springer Science and Business Media, 2012.
  • Hoos HH, Stützle T. Stochastic Local Search: Foundations and Applications. San Francisco, CA, USA: Elsevier, 2004.