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Volume 19, Issue 41 (12-2023)                   Marine Engineering 2023, 19(41): 85-104 | Back to browse issues page

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Gharechae A. Using Dung Beetle Optimizer (DBO) for optimizing the main dimensions of container ships. Marine Engineering 2023; 19 (41) :85-104
URL: http://marine-eng.ir/article-1-1086-en.html
Faculty member of Chabahar Maritime University
Abstract:   (340 Views)
In this research, with the help of the recently developed ِِDung Beetle Optimizer (DBO) algorithm, the main dimensions of container ships in terms of their loading capacity and their speed with the aim of minimum hydrodynamic resistance along with several constraints such as permissible range of main dimensions, hydrostatic stability, and specified underwater volume, has been optimized. For this purpose, the governing equations were extracted from Holtrop's experimental method. For verification, the results obtained from the DBO algorithm were compared with the results of the Optimization library of Maple software.  The optimization results on the dimensions of a container ship with a capacity of 1000 TEU showed that the hydrodynamic resistance of the optimized ship at a speed of 15 knots can be reduced by about 14% and at a speed of 19 knots by about 21%. Also, in constant displacement, with the increase of the vessel speed, the optimized vessel length increases, but its draft decreases. In particular, the purpose of this research is to introduce the capabilities of the DBO algorithm and its application in solving optimization problems in marine engineering.
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Type of Study: Research Paper | Subject: Ship Hydrodynamic
Received: 2023/12/6 | Accepted: 2024/01/27

References
1. 1. Qin, Y., Jin, L., Zhang, A. and He, B.,(2020), Rolling bearing fault diagnosis with adaptive harmonic kurtosis and improved bat algorithm, IEEE Transactions on Instrumentation and Measurement 70, p. 1-12. 2. Li, M., et al.,(2023), Fault diagnosis model of rolling bearing based on parameter adaptive AVMD algorithm, Applied Intelligence 53(3), p. 3150-3165. https://doi.org/10.1007/s10489-022-03562-9 3. Karami, H., et al.,(2019), Optimization of energy management and conversion in the water systems based on evolutionary algorithms, Neural Computing and Applications 31, p. 5951-5964.. https://doi.org/10.1007/s00521-018-3412-6 [DOI:10.1109/TIM.2020.3046913]
2. Singh, A. R., Ding, L., Raju, D. K., Raghav, L. P. and Kumar, R. S.,(2022), A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response, International Journal of Energy Research 46(4), p. 4301-4319. [DOI:10.1002/er.7427]
3. Li, J., Lei, Y. and Yang, S.,(2022), Mid-long term load forecasting model based on support vector machine optimized by improved sparrow search algorithm, Energy Reports 8, p. 491-497. [DOI:10.1016/j.egyr.2022.02.188]
4. Wei, D., Wang, J., Li, Z. and Wang, R.,(2021), Wind power curve modeling with hybrid copula and grey wolf optimization, IEEE Transactions on Sustainable Energy 13(1), p. 265-276. [DOI:10.1109/TSTE.2021.3109044]
5. Kennedy, J. and Eberhart, R.,(1995), in Proceedings of ICNN'95-international conference on neural networks. IEEE, vol. 4, p. 1942-1948. [DOI:10.1109/ICNN.1995.488968]
6. Liu, W., et al.,(2019), A novel sigmoid-function-based adaptive weighted particle swarm optimizer, IEEE transactions on cybernetics 51(2), p. 1085-1093. [DOI:10.1109/TCYB.2019.2925015] [PMID]
7. Chi, W., Ding, Z., Wang, J., Chen, G. and Sun, L.,(2021), A generalized Voronoi diagram-based efficient heuristic path planning method for RRTs in mobile robots, IEEE Transactions on Industrial Electronics 69(5), p. 4926-4937. [DOI:10.1109/TIE.2021.3078390]
8. Pehlivanoglu, Y. V. and Pehlivanoglu, P.,(2021), An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems, Applied Soft Computing 112, p. 107796. [DOI:10.1016/j.asoc.2021.107796]
9. Li, M., Xu, G., Fu, B. and Zhao, X.,(2022), Whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy, The Journal of Supercomputing, p. 1-31. [DOI:10.1007/s11227-021-04116-5]
10. Mirjalili, S., Mirjalili, S. M. and Lewis, A.,(2014), Grey wolf optimizer, Advances in engineering software 69, p. 46-61. [DOI:10.1016/j.advengsoft.2013.12.007]
11. Mirjalili, S. and Lewis, A.,(2016), The whale optimization algorithm, Advances in engineering software 95, p. 51-67. [DOI:10.1016/j.advengsoft.2016.01.008]
12. Heidari, A. A., et al.,(2019), Harris hawks optimization: Algorithm and applications, Future generation computer systems 97, p. 849-872. [DOI:10.1016/j.future.2019.02.028]
13. Ebadinezhad, S.,(2020), DEACO: Adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem, Engineering Applications of Artificial Intelligence 92, p. 103649. [DOI:10.1016/j.engappai.2020.103649]
14. Yang, K., You, X., Liu, S. and Pan, H.,(2020), A novel ant colony optimization based on game for traveling salesman problem, Applied Intelligence 50, p. 4529-4542. [DOI:10.1007/s10489-020-01799-w]
15. Liu, Y., Chen, S., Guan, B. and Xu, P.,(2019), Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy, Neurocomputing 332, p. 159-183. [DOI:10.1016/j.neucom.2018.12.021]
16. Huang, M., Lin, H., Yunkai, H., Jin, P. and Guo, Y.,(2012), Fuzzy control for flux weakening of hybrid exciting synchronous motor based on particle swarm optimization algorithm, IEEE Transactions on Magnetics 48(11), p. 2989-2992. [DOI:10.1155/2020/1390650]
17. Zeng, N., et al.,(2020), A dynamic neighborhood-based switching particle swarm optimization algorithm, IEEE transactions on cybernetics 52(9), p. 9290-9301. [DOI:10.1109/TCYB.2020.3029748] [PMID]
18. Guo, Q., Gao, L., Chu, X. and Sun, H.,(2022), Parameter identification for static var compensator model using sensitivity analysis and improved whale optimization algorithm, CSEE Journal of Power and Energy Systems 8(2), p. 535-547. [DOI:10.17775/CSEEJPES.2021.03540]
19. Zhong, C. and Li, G.,(2022), Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems, Expert Systems with Applications 192, p. 116432. 22. Chang, Z., et al.,(2021), 5G private network deployment optimization based on RWSSA in open-pit mine, IEEE Transactions on Industrial Informatics 18(8), p. 5466-5476. https://doi.org/10.1109/TII.2021.3132041 [DOI:10.1016/j.eswa.2021.116432]
20. Xue, J. and Shen, B.,(2023), Dung beetle optimizer: A new meta-heuristic algorithm for global optimization, The Journal of Supercomputing 79(7), p. 7305-7336. [DOI:10.1007/s11227-022-04959-6]
21. Polakis, M., Zachariadis, P., & de Kat, J. O. (2019). The energy efficiency design index (EEDI). Sustainable Shipping: A Cross-Disciplinary View, 93-135. [DOI:10.1007/978-3-030-04330-8_3]
22. Charchalis, A.,(2014), Determination of main dimensions and estimation of propulsion power of a ship, Journal of KONES 21(2), p. 39-44. [DOI:10.5604/12314005.1133863]
23. Jung, Y.-W. and Kim, Y.,(2019), Hull form optimization in the conceptual design stage considering operational efficiency in waves, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 233(3), p. 745-759. [DOI:10.1177/1475090218781115]
24. Jianping, C., Jie, X., You, G. and Li, X.,(2016), Ship Hull Principal Dimensions Optimization Employing Fuzzy Decision-Making Theory, Mathematical Problems in Engineering. [DOI:10.1155/2016/5262160]
25. Seif M. S., Kazemipour A., (2019). Ship Trim Optimization for the Reduction of Fuel Consumption, Marine Engineering, 15(29), p. 63-78, (In Persian). https://marine-eng.ir/article-1-684-fa.html
26. Mehrizi A., Tavakoli Dakhrabadi, M., Vafaee Sefat, A. and Seif, M. S., (2012), Hydrodynamic Optimization of Hull Form of High Speed Planing Craft by Multi Objective Genetic Algorithm in Calm Water, Marine Engineering 7(14), p. 45-58, (In Persian). http://marine-eng.ir/article-1-95-fa.html
27. Ebrahimi, A., (2023), Optimizing the dimensions of a container ship using the multi-objective genetic algorithm method, Journal of Marine Engineering 18(37), p. 70-78, (In Persian). http://marine-eng.ir/article-1-947-en.html
28. Guo, X., et al.,(2023), Speaker Recognition Based on Dung Beetle Optimized CNN, Applied Sciences 13(17), p. 9787. [DOI:10.3390/app13179787]
29. Zilong, W. and Peng, S.,(2023), A multi-strategy dung beetle optimization algorithm for optimizing constrained engineering problems, IEEE Access. [DOI:10.1109/access.2023.3313930]
30. Zhu, X., Ni, C., Chen, G. and Guo, J.,(2023), Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm, Sensors 23(12), p. 5616. [DOI:10.3390/s23125616] [PMID] []
31. Yin, Z. and Zinn-Björkman, L.,(2020), Simulating rolling paths and reorientation behavior of ball-rolling dung beetles, Journal of Theoretical Biology 486, p. 110106. [DOI:10.1016/j.jtbi.2019.110106] [PMID]
32. Notteboom, T. and Cariou, P.,(2009), Fuel surcharge practices of container shipping lines: Is it about cost recovery or revenue making, in Proceedings of the 2009 international association of maritime economists (IAME) conference. IAME Copenhagen, Denmark, p. 24-26. https://www.academia.edu/download/30824512/5-28_presentation.pdf
33. DNV AS, (2016). Container Ship Update 2016. https://issuu.com/dnvgl/docs/dnv_gl_container_ship_update__2016
34. Schneekluth, H. and Bertram, V.,(1998), Ship design for efficiency and economy, Butterworth-Heinemann Oxford, vol. 218. [DOI:10.1016/B978-0-7506-4133-3.X5000-2]
35. Charchalis, A. and Krefft, J.,(2009), Main dimensions selection methodology of the container vessels in the preliminary stage, Journal of KONES 16, p. 71-78. https://bibliotekanauki.pl/articles/241689
36. IMO (2008), International Code on Intact Stability, 2008, International Maritime Organization. https://www.imorules.com/IS2008.html
37. Woo, D., Choe, H. and Im, N.-K., (2021), Analysis of the Relationship between GM and IMO Intact Stability Parameters to Propose Simple Evaluation Methodology, Journal of Marine Science and Engineering 9(7), p. 735. [DOI:10.3390/jmse9070735]
38. ITTC, (2011), ITTC - Recommended Procedures and Guidelines, Resistance Test, ITTC. https://ittc.info/media/1217/75-02-02-01.pdf
39. Birk, L.,(2019), Fundamentals of ship hydrodynamics: Fluid mechanics, ship resistance and propulsion, John Wiley & Sons. [DOI:10.1002/9781119191575]
40. Holtrop, J.,(1988), A statistical resistance prediction method with a speed dependent form factor, Proceedings of the 17th Session BSHC, Varna 1, p. 3.1.
41. Guldhammer, H. and Harvald, S. A.,(1974), SHIP RESISTANCE-Effect of form and principal dimensions.(Revised), Danish Technical Press, Danmark, Danmarks Tekniske Hojskole, kademisk Forlag, St. kannikestrade 8, DK 1169 Copenhagen. http://resolver.tudelft.nl/uuid:1fa6c8b7-17c9-47ec-8a2f-d0afd56f51a0
42. Garrido, J.,(2019). Container-ship size: What dimensions can we expect to see? Pier Next. https://piernext.portdebarcelona.cat/en/mobility/container-ship-size/

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International Journal of Maritime Technology is licensed under a

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