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Learning-based ship design optimization approach

Cui, Hao and Turan, Osman and Sayer, Philip (2012) Learning-based ship design optimization approach. Computer-Aided Design, 44 (3). 186–195. ISSN 0010-4485

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    Abstract

    With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application.

    Item type: Article
    ID code: 35923
    Keywords: machine learning, ship design, structure optimization, structure analysis, multi-objective optimization, Naval architecture. Shipbuilding. Marine engineering, Computer Graphics and Computer-Aided Design, Industrial and Manufacturing Engineering, Computer Science Applications
    Subjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering
    Department: Faculty of Engineering > Naval Architecture and Marine Engineering
    Related URLs:
      Depositing user: Pure Administrator
      Date Deposited: 16 Nov 2011 14:47
      Last modified: 06 Sep 2014 13:22
      URI: http://strathprints.strath.ac.uk/id/eprint/35923

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