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2012 | 22 | 4 | 999-1009
Tytuł artykułu

Customized crossover in evolutionary sets of safe ship trajectories

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents selected aspects of evolutionary sets of safe ship trajectories-a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned within one minute, which enforces speeding up the optimisation process. During the development of the method the authors tested various problem-dedicated crossover operators to obtain the best performance. The results of that research are given here. The paper includes a detailed description of these operators as well as statistical simulation results and examples of experiment results.
Rocznik
Tom
22
Numer
4
Strony
999-1009
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-12-08
poprawiono
2012-05-28
Twórcy
  • Department of Marine Mechatronics, Faculty of Ocean Engineering and Ship Technology, Gdańsk University of Technology, Narutowicza 11-12, 80-233 Gdańsk, Poland
  • Department of Navigation, Faculty of Navigation, Gdynia Maritime University, Morska 81-87, 81-225 Gdynia, Poland
Bibliografia
  • Alba, E., Luna, F. and Nebro, A.J. (2004). Advances in parallel heterogeneous genetic algorithms for continuous optimisation, International Journal of Applied Mathematics and Computer Science 14(3): 317-333.
  • Belter, D. and Skrzypczyński, P. (2010). A biologically inspired approach to feasible gait learning for a hexapod robot, International Journal of Applied Mathematics and Computer Science 20(1): 69-84, DOI: 10.2478/v10006-010-0005-7.
  • Beyera, H., Schwefela, H. and Wegenerb, I. (2002). How to analyse evolutionary algorithms, Theoretical Computer Science 287(1): 101-130.
  • Cockroft, A. and Lameijer, J. (1993). A Guide to Collision Avoidance Rules, Butterworth-Heinemann Ltd., Oxford.
  • Eiben, A. and Schoenauer, M. (2002). Evolutionary computing, Information Processing Letters 82(1): 1-6.
  • Jóźwiak, L. and Postula, A. (2002). Genetic engineering versus natural evolution: Genetic algorithms with deterministic operators, Journal of Systems Architecture 48(1-3): 99-112.
  • Julstrom, B.A. (2004). Codings and operators in two genetic algorithms for the leaf-constrained minimum spanning tree problem, International Journal of Applied Mathematics and Computer Science 14(3): 385-396.
  • Kowalczuk, Z. and Białaszewski, T. (2006). Niching mechanisms in evolutionary computations, International Journal of Applied Mathematics and Computer Science 16(1): 59-84.
  • Krawiec, K., Jaskowski, W. and Szubert, M. (2011). Evolving small-board Go players using coevolutionary temporal difference learning with archives, International Journal of Applied Mathematics and Computer Science 21(4): 717-731, DOI: 10.2478/v10006-011-0057-3.
  • Mesghouni, K., Hammadi, S. and Borne, P. (2004). Evolutionary algorithms for job-shop scheduling, International Journal of Applied Mathematics and Computer Science 14(1): 91-103.
  • Michalewicz, Z. and Fogel, D. (2004). How to Solve It: Modern Heuristics, Springer-Verlag, Berlin.
  • Miquélez, T., Bengoetxea, E. and Larrañaga, P. (2004). Evolutionary computation based on Bayesian classifiers, International Journal of Applied Mathematics and Computer Science 14(3): 335-349.
  • Pradhan, S., Parhi, D., Panda, A. and Behera, R. (2006). Potential field method to navigate several mobile robots, Applied Intelligence 25(1): 321-333.
  • Śmierzchalski, R. and Michalewicz, Z. (2000). Modeling of a ship trajectory in collision situations at sea by evolutionary algorithm, IEEE Transactions on Evolutionary Computation 4(3): 227-241.
  • Styrcz, A., Mrozek, J. and Mazur, G. (2011). A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization, International Journal of Applied Mathematics and Computer Science 21(3): 559-566, DOI: 10.2478/v10006-011-0044-8.
  • Szłapczyńska, J. (2012). Multicriteria weather routing algorithm (MEWRA) applied to marine weather forecast and analysis tool-NaviWeather by NavSimTM , European Navigational Conference (ENC), Gdynia, Poland, (submitted).
  • Szłapczyński, R. (2006). A unified measure of collision risk derived from the concept of a ship domain, The Journal of Navigation 59(3): 477-490.
  • Szłapczyński, R. (2011). Evolutionary sets of safe ship trajectories: A new approach to collision avoidance, The Journal of Navigation 64(1): 169-181.
  • Szłapczyński, R. and Szłapczyńska, J. (2011). Evolutionary sets of safe ship trajectories: Problem dedicated operators, in P. Jędrzejowicz, N.T. Nguyen and K. Hoang (Eds.), Computational Collective Intelligence: Technologies and Applications, Part II, Lecture Notes in Artificial Intelligence, Vol. 6923, Springer-Verlag, Berlin/Heidelberg, pp. 231-240.
  • Troć, M. and Unold, O. (2010). Self-adaptation of parameters in a learning classifier system ensemble machine, International Journal of Applied Mathematics and Computer Science 20(1): 157-174, DOI: 10.2478/v10006-010-0012-8.
  • Tsou, M.C. and Hsueh, C.K. (2010). The study of ship collision avoidance route planning by ant colony algorithm, Journal of Marine Science and Technology 18(5): 746-756.
  • Tsou, M.C., Kao, S.L. and Su, C.M. (2010). Decision support from genetic algorithms for ship collision avoidance route planning and alerts, The Journal of Navigation 63(1): 167-182.
  • Xue, Y., Lee, B. and Han, D. (2009). Automatic collision avoidance of ships, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 223(1): 33-46.
  • Zeng, X. (2003). Evolution of the safe path for ship navigation, Applied Artificial Intelligence 17(2): 87-104.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv22z4p999bwm
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