Parameter Tuning of Complex Systems Modeled in Agent Based Modeling and Simulation
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.
[1] Di Marzo Serugendo, Giovanna, Gleizes, Marie-Pierre, Karageorgos, Anthony, “Self-organising Software from Natural to Artificial Adaptation”, 1st editör, Heidelberg, Berlin: Springer-Verlag, 2011, pp. 7-32.
[2] B. 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.
[3] D. 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.
[4] F. 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.
[5] J. H. Holland, Adaptation in natural and artificial System, Ann Arbor: The University of Michigan Press, MA USA, 1992, ch 3.
[6] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization”, Proc. of the IEEE Int. Conference on Neural Networks, Western Australia, 1995, 1942-1948.
[7] D. 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.
[8] X. S. Yang, Firefly algorithm, Nature-Inspired Metaheuristic Algorithms, 2nd Ed., Luniver Press, Ed.: Xin-She Yang, Frome UK, 2008, PP. 79-90.
[9] K. F. Man, K. S. Tang and S. Kwong, Modifications to Genetic Algorithms, Genetic Algorithms, Springer London, Hong Kong, 1999, pp. 23-44.
[10] N. Adar1, G. Kuvat, “Paralel Genetik Algoritmalarda Farklılık Ve Geçirgenlik”, Dumlupınar üniversitesi, fen bilimleri ensititüsü dergisi, vol. 27, April 2012, pp. 55-66.
[11] S. Tamer, C. Karakuzu, “Parçacık Sürüsü Optimizasyon Algoritması ve Benzetim Örnekleri”, ELECO 2006 Elektrik-Elektronik-Bilgisayar Sempozyumu, Elektronik Bildirileri Kitabı, Bursa, 2006, pp. 302-306.
[12] J. C. Bansal, P. K. Singh, M. Saraswat, “Inertia Weight Strategies in Particle Swarm Optimization”, 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, 2011, pp. 633-640.
[13] A. Singh, “An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem”, Applied Soft Computing, University of Hyderabad, Andhra Pradesh, vol. 9, 2009, pp. 625- 631.
[14] F. Kang, J. Li and Q. Xu, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Comput. Struct. 87, Dalian, 2009, 861-870 pp.
[15] U. Wilensky, NetLogo wolf sheep predation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 1997.
[16] İ. Çakırlar, Çakırlar, “Etmen Temelli Benzetimler İçin Test Güdümlü Bir Yaklaşım Geliştirilmesi”, Ege University, Computer Engineering Department, PhD Thesis, İzmir, 2014.
[1] Di Marzo Serugendo, Giovanna, Gleizes, Marie-Pierre, Karageorgos, Anthony, “Self-organising Software from Natural to Artificial Adaptation”, 1st editör, Heidelberg, Berlin: Springer-Verlag, 2011, pp. 7-32.
[2] B. 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.
[3] D. 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.
[4] F. 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.
[5] J. H. Holland, Adaptation in natural and artificial System, Ann Arbor: The University of Michigan Press, MA USA, 1992, ch 3.
[6] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization”, Proc. of the IEEE Int. Conference on Neural Networks, Western Australia, 1995, 1942-1948.
[7] D. 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.
[8] X. S. Yang, Firefly algorithm, Nature-Inspired Metaheuristic Algorithms, 2nd Ed., Luniver Press, Ed.: Xin-She Yang, Frome UK, 2008, PP. 79-90.
[9] K. F. Man, K. S. Tang and S. Kwong, Modifications to Genetic Algorithms, Genetic Algorithms, Springer London, Hong Kong, 1999, pp. 23-44.
[10] N. Adar1, G. Kuvat, “Paralel Genetik Algoritmalarda Farklılık Ve Geçirgenlik”, Dumlupınar üniversitesi, fen bilimleri ensititüsü dergisi, vol. 27, April 2012, pp. 55-66.
[11] S. Tamer, C. Karakuzu, “Parçacık Sürüsü Optimizasyon Algoritması ve Benzetim Örnekleri”, ELECO 2006 Elektrik-Elektronik-Bilgisayar Sempozyumu, Elektronik Bildirileri Kitabı, Bursa, 2006, pp. 302-306.
[12] J. C. Bansal, P. K. Singh, M. Saraswat, “Inertia Weight Strategies in Particle Swarm Optimization”, 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, 2011, pp. 633-640.
[13] A. Singh, “An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem”, Applied Soft Computing, University of Hyderabad, Andhra Pradesh, vol. 9, 2009, pp. 625- 631.
[14] F. Kang, J. Li and Q. Xu, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Comput. Struct. 87, Dalian, 2009, 861-870 pp.
[15] U. Wilensky, NetLogo wolf sheep predation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 1997.
[16] İ. Çakırlar, Çakırlar, “Etmen Temelli Benzetimler İçin Test Güdümlü Bir Yaklaşım Geliştirilmesi”, Ege University, Computer Engineering Department, PhD Thesis, İzmir, 2014.
@article{"International Journal of Information, Control and Computer Sciences:76800", author = "Rabia Korkmaz Tan and Şebnem Bora", title = "Parameter Tuning of Complex Systems Modeled in Agent Based Modeling and Simulation", abstract = "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.
", keywords = "Parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems.", volume = "11", number = "12", pages = "1314-10", }
