Nguyen Hoang Mai, Tran Van Dung

Last modified: 09.04.2019


The development of the AI, IoT, and Big Data have to become strongly apply to discrete event strings systems. That are modern developments of the world. Therefore, we have to have an advanced method to develop adaptive applications, especially with MIMO discrete event systems. There is a limit while using a continuous calculation to control systems because the big calculation is an obstacle. So we have to find an optimization method to reduce the number of parameters in the calculation at any time. We could do it by choice the main parameters and except auxiliary parameters. In this paper, we introduce a Football Team Optimization (FTO) method, which is a new method to do optimization problem while control with many parameters system. The application and analysis to compare any method as PSO, traditional PID, which takes out the difference of this algorithm.


Football team model; traditional PID; discrete event system; robot team; self-organize


[1]    W. Deng, H. M. Zhao, J.J. Liu, X.L. Yan, Y.Y. Li, L.F. Yin, C.H. Ding, "An improved CACO algorithm based on the adaptive method and multi-variant strategies", Soft Computing, vol. 19 no. 3, (2015), pp. 701- 713.

[2]    D. N. Wilke. Analysis of the particle swarm optimization algorithm, Master's Dissertation, University of Pretoria, 2005.

[3]    Natika W. Newton, Understanding and Self-Organization,  Front Syst Neurosci. 2017,  Mar 2. doi: 10.3389/ fnsys. 2017.00008

[4]    D. N. Wilke, S. Kok, and A. A. Groenwold, Comparison of linear and classical velocity update rules in particle swarm optimization: notes on diversity, International Journal for Numerical Methods in Engineering, Vol. 70, No. 8, pp. 962–984, 2007.

[5]    P. Engelbrecht. Fundamentals of Computational Swarm Intelligence. Wiley, 2005.

[6]    Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of IEEE congress evolutionary computation, San Diego, CA, pp 84–88 10.

[7]    Kennedy J (2006) Swarm intelligence. In: Handbook of nature-inspired and innovative computing. Springer, New York, pp 187–219 13.

[8]    Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, UK 14. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: The proceedings of evolutionary programming VII (EP98), pp 591–600 15.

[9]    W. Deng, R. Chen, B. He, Y.Q. Liu, L.F. Yin, J.H. Guo, “A novel two-stage hybrid swarm intelligence optimization algorithm and application”, Soft Computing, vol. 16, no. 10, (2012), pp. 1707-1722

[10]  Pedersen MEH (2010) Good parameters for particle swarm optimization. Hvass Laboratories Technical Report HL1001

[11]  Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Third world congress on nature and biologically inspired computing (NaBIC 2011), IEEE, Salamanca, Spain, pp 640–647

[12]  Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289 42 2 Particle Swarm Optimization

[13]  Qian X, Cao M, Su Z, Chen J (2012) A hybrid particle swarm optimization (PSO)-simplex algorithm for damage identification of delaminated beams. Math Probl Eng, Article ID 607418, p 11

[14]  Chen WN, Zhang J, Chung HSH, Zhong WL, Wu WG, Shi Y (2010) A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Trans Evol Comput 14 (2):278–300

[15]  Gomes MH (2011) Truss optimization with dynamic constraints using a particle swarm algorithm. Expert Syst Appl 38:957–968

[16]  Kaveh A, Zolghadr A (2011) Shape and size optimization of truss structures with frequency constraints using enhanced charged system search algorithm. Asian J Civil Eng 12:487–509