基于改进飞蛾扑火优化算法的船机桨匹配设计研究

doi:10.11896/jsjkx.230500157

计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230500157-9.doi: 10.11896/jsjkx.230500157

基于改进飞蛾扑火优化算法的船机桨匹配设计研究

陈振霖, 罗亮, 郑龙, 姬胜晨, 陈顺怀

高性能船舶技术教育部重点实验室武汉 430063
武汉理工大学船海与能源动力工程学院武汉 430063

发布日期:2024-06-06
通讯作者: 罗亮(luoliang@whut.edu.com)
作者简介:(319018@whut.edu.cn)
基金资助:
国家自然科学基金(52101368)

Study on Matching Design of Ship Engine and Propeller Based on Improved Moth-Flame Optimization Algorithm

CHEN Zhenlin, LUO Liang, ZHENG Long, JI Shengchen, CHEN Shunhuai

School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China
Key Laboratory of High Performance Marine Technology,Ministry of Education,Wuhan 430063,China

Published:2024-06-06
About author:CHEN Zhenlin,born in 1998,postgra-duate.His main research interests include intelligent computing and simulation integration.
LUO Liang,born in 1980,Ph.D,asso-ciate professor.His main researchin-terests include digital ship design,system simulation integration,intelligent computing,and more.
Supported by:
National Natural Science Foundation of China(52101368).

摘要/Abstract

摘要： 基于改进飞蛾扑火优化(Improved Moth-Flame Optimization,IMFO)算法,以两艘现有船舶为计算实例,展开了综合考虑螺旋桨推进效率、空泡性能和桨叶强度的船机桨匹配工作。以遗传算法(Genetic Algorithm,GA)和原始飞蛾扑火优化(Moth-Flame Optimization,MFO)算法为对比算法,分析了IMFO辅助船机桨匹配工作时的性能。数值实验的结果表明,在解决船机桨匹配问题时,IMFO算法的收敛时间相比GA算法在两个算例中分别缩短了44.24%和54.14%,相比MFO算法分别缩短了23.9%和23.12%。此外,在求解精度方面,在计算示例1中,IMFO算法相比GA算法和MFO算法略有提升;而在计算示例2中,IMFO算法相比GA算法提高了3.66%,较MFO算法提高了0.98%。最后,通过对两个算例的可行解空间进行可视化表示,进一步讨论了IMFO算法的求解性能。上述结果对比证明了IMFO算法具备强大的全局搜索能力,在解决船机桨匹配问题时具有良好的竞争力和鲁棒性。

关键词: 改进飞蛾扑火优化算法, 优化设计, 群智能优化算法, 船机桨匹配, 船用螺旋桨

Abstract: This paper develops an improved moth-flame optimization(IMFO) algorithm for the ship propeller-matching problem,which comprehensively considers propeller efficiency,cavitation,and strength for two existing ships as calculation examples.Genetic algorithm(GA) and the original moth-flame optimization(MFO) algorithm are used as comparison algorithms to analyze the performance of the IMFO-assisted propeller-matching task.Numerical experiment results show that the convergence time of the IMFO algorithm in solving the propeller-matching problem is reduced by 44.24% and 54.14% compared to the GA algorithm in the two examples,and by 23.9% and 23.12% compared to the MFO algorithm,respectively.In addition,in terms of solution accuracy,the IMFO algorithm is slightly better than the GA and MFO algorithms in calculation example 1.In calculation example 2,the IMFO algorithm is improved by 3.66% compared to the GA algorithm and by 0.98% compared to the MFO algorithm.Finally,by visualizing the feasible solution space of the two examples,the performance of the IMFO algorithm is further discussed.The above results demonstrate that the IMFO algorithm has strong global search capability and is competitive and robust in solving the propeller-matching problem.

Key words: Improved moth-flame optimization algorithm, Optimized design, Swarm intelligence optimization algorithm, Matching of ship engine and propeller, Marinepropeller

中图分类号:

U664.33

陈振霖, 罗亮, 郑龙, 姬胜晨, 陈顺怀. 基于改进飞蛾扑火优化算法的船机桨匹配设计研究[J]. 计算机科学, 2024, 51(6A): 230500157-9. https://doi.org/10.11896/jsjkx.230500157

CHEN Zhenlin, LUO Liang, ZHENG Long, JI Shengchen, CHEN Shunhuai. Study on Matching Design of Ship Engine and Propeller Based on Improved Moth-Flame Optimization Algorithm[J]. Computer Science, 2024, 51(6A): 230500157-9. https://doi.org/10.11896/jsjkx.230500157

参考文献

[1]SHENG Z B,LIU Y Z.Principles of ships(below)[M].Shang-hai Jiaotong University Press,2004.
[2]ZENG Z B,DING E B,TANG D H.Optimal Design of ShipPropeller Based on BP Artificial Neural Network and Genetic Algorithm[J].Ship Mechanics,2010,14(1):20-27.
[3]WANG P,HUANG S,ZHU Z Q.Application of Swarm Intelligence Algorithm in Optimal Design of Propeller Parameters[J].Computer Science,2013,40(2):73-76.
[4]JANG T S,KINOSHITA T,YAMAGUCHI H.A new functional optimization method applied to the pitch distribution of a marine propeller[J].Journal of Marine Science and Technology,2001,6(1):23-30.
[5]TAKEKOSHI Y,KAWAMURA T,YAMAGUCHI H,et al.Study on the design of propeller blade sections using the optimization algorithm[J].Journal of Marine Science and Techno-logy,2005,10(2):70-81.
[6]WANG C,HAN K,SUN C,et al.Optimal Design and Parameter Analysis of Marine Propeller[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2020,448(4):102-107.
[7]ZENG Z.Optimal Design of Propeller Blade Sections of High-Speed Surface Ships under Multiple Operating Conditions[J].China Shipbuilding,2018,59(1):26-35.
[8]HUANG B,XIONG Y,WANG B.Optimization of propellerskew distribution based on particle swarm optimization[J].China Ship Research,2016(6).
[9]LIU C,YE C.A Novel Bionic Swarm Intelligent Optimization Algorithm:Firefly Algorithm[J].Computer Application Research,2011,28(9):3295-3297.
[10]RAJABIOUN R.Cuckoo Optimization Algorithm[J].Applied Soft Computing Journal,2011,11(8):5508-5518.
[11]KARABOGA D,BASTURK B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC) algorithm[J].Global Optimization,2007,39(3):459-471.
[12]MIRJALILI.S.Moth-flame optimization algorithm:A novel nature-inspired heuristic paradigm[J].Knowledge-Based Systems,2015,89:228-249.
[13]LIU C.Multidisciplinary Optimal Design of Marine PropellerBased on Complex System Modeling Theory[D].Jiangsu University of Science and Technology,2014.
[14]YANG L C,YANG C J,LI X B.Research on Optimal Design of Propeller Based on Multi-objective Evolutionary Algorithm and Decision-Making Technology[J].China Shipbuilding,2019,60(3):12.
[15]DOKEROGLU T.A survey on new generation etaheuristic algorithms[J].Computers & Industrial Engineering,2019,137(C):106040-106040.

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基于改进飞蛾扑火优化算法的船机桨匹配设计研究

Study on Matching Design of Ship Engine and Propeller Based on Improved Moth-Flame Optimization Algorithm

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