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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

A seakeeping analysis in the frequency domain is presented to predict the motion response of an airlifted vessel (ALV) in waves. The ALV is supported by pressurised air in two separate cushion chambers; the pressure variation in the cushions has a significant effect on the motions of the vessel. The adiabatic gas law is used to couple cushion pressure and the free-surface elevation of water inside the chamber. Attention is focused on the waves generated by the pressure, and a method is presented to compute the corresponding free-surface elevation. New numerical schemes are proposed for calculating the threedimensional free-surface elevation for the four wave numbers. Numerical results of the free-surface elevation, escape area, escape volume and motion responses of the ALV are provided. & 2008 Elsevier Ltd. All rights reserved

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