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Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm: Part II Computational simulations

References Abraham, A., Jain, L. and Goldberg, R., 2005. Evolutionary Multiobjective Optimization. Springer. Back, T., 1996. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York. Binh, T. T. and Korn, U., 1997. MOBES: A Multiobjective Evolution Strategy for Constrained Optimization Problems. In: The Third International Conference on Genetic Algorithms (Mendel 97) , 25-27 June 1997, Brno, Czech Republic, 176-182. Darwin, Ch

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Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm

References Abraham, A., Jain, L. and Goldberg, R., 2005. Evolutionary Multiobjective Optimization. Springer. Back, T., 1996. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York. Binh, T. T. and Korn, U., 1997. MOBES: A Multiobjective Evolution Strategy for Constrained Optimization Problems. In: The Third International Conference on Genetic Algorithms (Mendel 97) , 25-27 June 1997, Brno, Czech Republic, 176-182. Darwin, Ch

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Evolutionary sets of safe ship trajectories with speed reduction manoeuvres within traffic separation schemes

on Marine Navigation and Safety of Sea Transportation, 1, no. 1., 11-18, 2007. 8. Statheros T., Howells G. and McDonald-Maier K.: Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques, The Journal of Navigation, 61, 129-142, 2008. 9. Smierzchalski R., Michalewicz Z.: Modelling of a Ship Trajectory in Collision Situations at Sea by Evolutionary Algorithm, IEEE Transactions on Evolutionary Computation. No. 3 Vol. 4, 227-241, 2000. 10. Szlapczynski R.: Evolutionary Sets of Safe Ship

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Comparison of Fuzzy System with Neural Aggregation FSNA with Classical TSK Fuzzy System in Anti-Collision Problem of USV

. Szymak, T. Praczyk, “Using Neural-Evolutionary-Fuzzy Algorithm for Anti-collision System of Unmanned Surface Vehicle”, in Proceedings of the 17th International Conference on Methods and Models in Automation and Robotics , pp. 286-290, 2012. 16. P. Szymak, “Course Control of Unmanned Surface Vehicle”, Solid State Phenomena , Vol. 196, pp. 117-123, 2013. 17. T. Takagi, M. Sugeno, “Fuzzy Identification of Systems and its Application to Modelling and Control”, IEEE Transactions on Systems, Man and Cybernetics , vol. 15, pp. 116-132, 1985. 18. J. Vieira

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Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm

Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm

Real ship structural design problems are usually characterized by presence of many conflicting objectives. Simultaneously, a complete definition of the optimal structural design requires a formulation of size-topology-shape-material optimization task unifying the optimization problems from these four areas and giving an effective solution of this problem. So far, a significant progress towards the solution of this problem has not been obtained. An objective of the present paper was to develop an evolutionary algorithm for multi-objective optimization of the structural elements of the large spatial sections of ships. Selected elements of the multi-criteria optimization theory have been presented in details. Methods for solution of the multi-criteria optimization problems have been discussed with the focus on the evolutionary optimization algorithms. In the paper an evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area on a high speed vehicle-passenger catamaran structure with several design variables, such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinals and transversal members. Details of the computational models were at the level typical for conceptual design. Scantlings were analyzed using the selected rules of a classification society. The results of numerical experiments with the use of the developed algorithm are presented. They show that the proposed genetic algorithm can be an efficient multi-objective optimization tool for ship structures optimization.

The paper will be published in three parts: Part I: Theoretical background on evolutionary multi-objective optimization, Part II: Computational investigations, and Part III: Analysis of the results.

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Swarm Intelligence Approach to Safe Ship Control

. 16, No.1, 2011, p. 84-99. 19. Pluciński M.: Application of the Ant Colony Algorithm for the Path Planning. Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems, 2005, p. 345-352. 20. Szłapczyński R.: A New Method of Ship Routing on Raster Grids, with Turn Penalties and Collision Avoidance. The Journal of Navigation, Vol. 59, 2006, p. 27-42. 21. Szłapczyński R.: Evolutionary approach to ship’s trajectory planning within Traffic Separation Schemes. Polish Maritime Research, Vol. 19, Issue

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Modelling of Ship’s Trajectory Planning in Collision Situations by Hybrid Genetic Algorithm

BIBLIOGRAPHY 1. Bazarra M.S., Shetty L.M.: Nonlinear Programming: Theory and Algorithms. John Wiley and Sons, New York, 1979, pp. 1-360. 2. Benjamin M. R., Curcio J. A.: COLREGS-based navigation of autonomous marine vehicles. Autonomous Underwater Vehicles IEEE/OES. IEEE, 2004, pp. 32-39. 3. Burmeister, H.C., Bruhn W. and Porathe T.: Autonomous unmanned merchant vessel and its contribution towards the e-navigation implementation. the MUNIN perspective. Ocean Engineering. 1, 2014, pp. 1-13. 4. Goldberg D.: Genetic Algorithms in Search

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Structural weight minimization of high speed vehicle-passenger catamaran by genetic algorithm

References Back T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, 1996 Bendsoe M.P., Kikuchi N.: Generating Optimal Topologies in Structural Design Using a Homogenization Method. Computer Methods in Applied Mechanics and Engineering 1988 Coley D.A.: An Introduction to Genetic Algorithms for Scientists and Engineers. World Scientific, Singapore, 1999 Carroll D.L.: Genetic Algorithms and Optimizing Chemical Oxygen

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Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

intelligence. Artificial Intelligence Review, 31(1), 61-85. 18. Celal Ozturk, Emrah Hancer, Dervis Karaboga, 2015. Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Analysis and Applications, 18(3), 587-599. 19. J Sun, W Fang, X Wu,2014. Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection. Evolutionary Computation, 20(3), 349-393. 20. Miha Mlakar, Dejan Petelin, Tea Tušar, Bogdan Filipič, 2015. GP-DEMO: Differential

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Research and Application of Rule Updating Mining Algorithm for Marine Water Quality Monitoring Data

. R. Penido, A Method for Sizing of Industrial Electrical Systems using Genetic Algorithm, IEEE Latin America Transactions, Vol. 14, No. 2, pp. 681-686, 2016. 9. Y. Tominaga, Y. Okamoto, and S. Wakao, Binary-based Topology Optimization of Magnetostatic Shielding by a Hybrid Evolutionary Algorithm Combining Genetic Algorithm and Extended Compact Genetic Algorithm, IEEE Transactions on Magnetics, No. 49, No. 5, pp. 2093-2096, 2013. 10. T. Lu, and J. Zhu, Genetic Algorithm for Energy-Efficient QoS Multicast Routing, IEEE Communications

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