Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230500157-9.doi: 10.11896/jsjkx.230500157

• Artificial Intelligenc • Previous Articles     Next Articles

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   

  1. 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).

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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

CLC Number: 

  • U664.33
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