The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.<\/p>\r\n","references":"[1]\tDi Marzo Serugendo, Giovanna, Gleizes, Marie-Pierre, Karageorgos, Anthony, \u201cSelf-organising Software from Natural to Artificial Adaptation\u201d, 1st edit\u00f6r, Heidelberg, Berlin: Springer-Verlag, 2011, pp. 7-32.\r\n[2]\tB. Calvez, G. Hutzler, \"Automatic tuning of agent-based models using genetic algorithms\", Proceedings of the 6th International Workshop on Multi-Agent Based Simulation (MABS'05), Springer, Utrecht, The Netherland, 2005, pp. 41-57.\r\n[3]\tD. S. Bolme, J. R. Beveridge, B. A. Draper, P. J. Phillips, Y. M. Lui. \"Automatically Searching for Optimal Parameter Settings Using a Genetic Algorithm\", Computer Vision Systems - 8th International Conference, {ICVS}, Sophia Antipolis, 2011, pp. 213-222.\r\n[4]\tF. Dobslaw, \"A Parameter Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks\", Proceeding of the International Conference on Computer Mathematics and Natural Computing, Rome, 2010.\r\n[5]\tJ. H. Holland, Adaptation in natural and artificial System, Ann Arbor: The University of Michigan Press, MA USA, 1992, ch 3.\r\n[6]\tJ. Kennedy and R. C. Eberhart, \u201cParticle Swarm Optimization\u201d, Proc. of the IEEE Int. Conference on Neural Networks, Western Australia, 1995, 1942-1948.\r\n[7]\tD. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optimization, Springer US, 39, November 2007, pp. 459-471.\r\n[8]\tX. S. Yang, Firefly algorithm, Nature-Inspired Metaheuristic Algorithms, 2nd Ed., Luniver Press, Ed.: Xin-She Yang, Frome UK, 2008, PP. 79-90.\r\n[9]\tK. F. Man, K. S. Tang and S. Kwong, Modifications to Genetic Algorithms, Genetic Algorithms, Springer London, Hong Kong, 1999, pp. 23-44.\r\n[10]\tN. Adar1, G. Kuvat, \u201cParalel Genetik Algoritmalarda Farkl\u0131l\u0131k Ve Ge\u00e7irgenlik\u201d, Dumlup\u0131nar \u00fcniversitesi, fen bilimleri ensitit\u00fcs\u00fc dergisi, vol. 27, April 2012, pp. 55-66.\r\n[11]\tS. Tamer, C. Karakuzu, \u201cPar\u00e7ac\u0131k S\u00fcr\u00fcs\u00fc Optimizasyon Algoritmas\u0131 ve Benzetim \u00d6rnekleri\u201d, ELECO 2006 Elektrik-Elektronik-Bilgisayar Sempozyumu, Elektronik Bildirileri Kitab\u0131, Bursa, 2006, pp. 302-306.\r\n[12]\tJ. C. Bansal, P. K. Singh, M. Saraswat, \u201cInertia Weight Strategies in Particle Swarm Optimization\u201d, 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, 2011, pp. 633-640.\r\n[13]\t A. Singh, \u201cAn artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem\u201d, Applied Soft Computing, University of Hyderabad, Andhra Pradesh, vol. 9, 2009, pp. 625- 631.\r\n[14]\tF. Kang, J. Li and Q. Xu, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Comput. Struct. 87, Dalian, 2009, 861-870 pp.\r\n[15]\tU. Wilensky, NetLogo wolf sheep predation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 1997.\r\n[16]\t\u0130. \u00c7ak\u0131rlar, \u00c7ak\u0131rlar, \u201cEtmen Temelli Benzetimler \u0130\u00e7in Test G\u00fcd\u00fcml\u00fc Bir Yakla\u015f\u0131m Geli\u015ftirilmesi\u201d, Ege University, Computer Engineering Department, PhD Thesis, \u0130zmir, 2014.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 132, 2017"}