Learning-based ship design optimization approach

Hao Cui, Osman Turan, Philip Sayer

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)
1432 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)186–195
Number of pages10
JournalComputer-Aided Design
Issue number3
Early online date1 Aug 2011
Publication statusPublished - Mar 2012


  • machine learning
  • ship design
  • structure optimization
  • structure analysis
  • multi-objective optimization


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