Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review

  • Željko Kanović Department for Traffic Engineering Faculty of Technical Sciences, University of Novi Sad Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
  • Vladimir Bugarski Department for Traffic Engineering Faculty of Technical Sciences, University of Novi Sad
  • Todor Bačkalić Department for Traffic Engineering Faculty of Technical Sciences, University of Novi Sad
Keywords: ship lock, fuzzy expert system, particle swarm optimization, artificial bee colony optimization, genetic algorithm

Abstract

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.

References

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

How to Cite
1.
Kanović Željko, Bugarski V, Bačkalić T. Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review. Promet [Internet]. 1 [cited 2022Aug.11];26(1):23-1. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/1475
Section
Articles