This paper presents the comparison of some well-known global optimization techniques in optimization of an expert system controlling a ship locking process. The purpose of the comparison is to find the best algorithm for optimization of membership function parameters of fuzzy expert system for the ship lock control. Optimization was conducted in order to achieve better results in local distribution of ship arrivals, i.e. shorter waiting times for ships and less empty lockages. Particle swarm optimization, artificial bee colony optimization and genetic algorithm were compared. The results shown in this paper confirmed that all these procedures show similar results and provide overall improvement of ship lock operation performance, which speaks in favour of their application in similar transportation problem optimization.
Partenscky, H.W.: Inland waterways: lock installations. (Binnenverkehrswasserbau: Schleusenanlagen – in original). Berlin: Springer; 1986.
Bačkalić, T.: Traffic control on artificial waterways of limited dimensions in function of its throughput capacity (Upravljanje saobraćajem na veštačkim plovnim putevima ograničenih dimenzija u funkciji njihove propusne sposobnosti - in original). Novi Sad: University of Novi Sad, Faculty of technical sciences, PhD thesis; 2000.
Smith, L.D., Sweeney, I.I.D.C., Campbell, J.F.: Simulation of alternative approaches to relieving congestion at locks in a river transportation system. Journal of the Operational Research Society.2009; 60:519-533
Radmilović, Z., Maraš, V., Jovanović, S.: Ship lock as general queuing system with batch arrivals and batch service. PROMET – Traffic&Transportation. 2012;19(6):343-352
Bugarski, V., Bačkalić, T., Kuzmanov, U.: Fuzzy decision support system for ship lock control. Expert Systems with Applications. 2013; 40(10):3953-3960 http://dx.doi.org/10.1016/j.eswa.2012.12.101
Campbell, J.F., Smith, L.D., Sweeney, I.I.D.C., Mundy, R., Nauss, R.M.: Decision tools for reducing congestion at locks on the upper Mississippi river. Proceedings of the 40th Hawaii International Conference on System Sciences. 2007; 55-58
Kecman, V.: Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. Boston, MA: Massachusetts Institute of Technology; 2001
Kosko, B.: Fuzzy thinking: the new science of fuzzy logic. New York: Hyperion; 1993
Comes, T., Hiete, M., Wijngaards, N., Schultmann, F.: Decision maps: a framework for multi-criteria decision support under severe uncertainty. Decision Support Systems. 2011; 52(1):108-118
Onieva, E., Milanes, V., Villagra, J., Perez, J., Godoy, J.: Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Systems with Applications. 2012; 39(18):13148-13157. http://dx.doi.org/10.1016/j.eswa.2012.05.087
Teodorović, D., Vukadinović, K.: Traffic control and transport planning: a fuzzy sets and neural networks approach. Norwel, MA: Kluwer Academia Publishers; 1998
Castanho, M.J.P., Hernandes, F., De Re, A.M., Rautenberg, S., Billis, A.: Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems with Applications. 2013; 40(2):466-470. http://dx.doi.org/10.1016/j.eswa.2012.07.046
Dasgupta, D., Michalewicz, Z.: Evolutionary algorithms in engineering applications. Berlin: Springer Verlag; 1997
Yunusoglu, M.G., Selim, H.: A fuzzy rule based expert system for stock evaluation and portfolio construction: an application to Istanbul Stock Exchange. Expert Systems with Applications. 2013; 40(3):908-920. http://dx.doi.org/10.1016/j.eswa.2012.05.047
Teodorović, D., Dell’Orco, M.: Bee colony optimization–a cooperative learning approach to complex transportation problems. Advanced OR and AI Methods in Transportation. Proceedings of 16th Mini–EURO Conference and 10th Meeting of EWGT. 2005 Sept.; 51-60.
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence. 2007; 20:89-99.
Kanović, Ž., Rapaić, M., Jeličić, Z.: Generalized particle swarm optimization algorithm: theoretical and empirical analysis with application in fault detection. Applied Mathematics and Computation. 2011; 217:10175-10186. http://www.sciencedirect.com/science/article/pii/S0096300311006680
Ting, C.J., Schonfield, P.: Control alternatives at a waterway lock. Journal of Waterway, Port, Coastal and Ocean Engineering. 2001; 127(2):89-96. http://dx.doi.org/10.1061/(ASCE)0733-950X(2001)127:2(89)
International Navigation Association. Inland Navigation Commission. Guidelines and recommendations for river information services. International Navigation Association; 2004.
Willems, C., Schmorak, N.: River Information Services on the way to maturity. Proc. on 32nd PIANC International Navigation Congress, Liverpool, United Kingdom, 10-14 May 2010. 2010; 1:285-297
Yager, R.R., Filev, D.P.: Essentials of fuzzy modeling and control. New York: John Wiley and Sons; 1994
Zimmermann, H.J.: Fuzzy set theory and its applications. 4th ed. Dordrecht: Kluwer Academic Publishers; 2001
Camps-Valls, G., Martín-Guerrero, J.D., Rojo-Alvarez, J.L., Soria-Olivas, E.: Fuzzy sigmoid kernel for support vector classifiers. Neurocomputing. 2004; 62:501-506
Jang, J-S.R., Sun, C-T., Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. New Jersey: Prentice Hall; 1997
Nguyen, H.T., Sugeno, M.: Fuzzy systems: modelling and control. Dordrecht: Kluwer Academic Publishers; 1998
Jantzen, J.: Foundations of fuzzy control. New Jersey: John Wiley and Sons; 2007
Lancaster, S.: Fuzzy logic controllers. Portland: Maseeh College of Engineering and Computer Science at PSU; 2008
Collette, Y., Siarry, P.: Multiobjective optimization: principles and case studies. Berlin: Springer; 2004
Rao, R.V., Patel, V.: Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence.2013; 26(1):430-445. http://dx.doi.org/10.1016/j.engappai.2012.02.016
Holland, J.: Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press; 1975
Michalewicz, Z.: Genetic algorithms + data structures = evolution programming. 3rd ed. Berlin: Springer Verlag; 1999
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia. 1995; 1942-1948
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. Proceedings of IEEE International Congress on Evolutionary Computation.1999; 3:101-106
Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. 2002; 6(1):58-73
Rapaić, M., Kanović, Ž.: Time-varying PSO: convergence analysis, convergence related parameterization and new parameter adjustment schemes. Information Processing Letters. 2009; 109(1):548-552 http://www.sciencedirect.com/science/article/pii/S0020019009000350#
Ratnaweera, A., Saman, K.H., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation. 2004; 8(3):240-255
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report- TR06. Kayseri, Turkey: Erciyes University; 2005
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
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