Design Knowledge Extraction Framework and Its Application in Multi-Objective Ship Optimization
Ocean engineering(2023)
Abstract
Currently, hull form optimization performs design space searches through an optimization algorithm to find the optimal solution and discusses the optimal result only while ignoring a large amount of optimized data. In the optimized data, there are implicit relationships between variables and between the variables and objective function, which can contribute to a better understanding of optimization problems. For the above problems, this paper studies the optimized data in hull form optimization and proposes a set of design knowledge extraction methods suitable for multi-objective ship optimization; in addition, we study the optimization of ship form lines under various working conditions using a 7500-ton inland twin-skeg bulk carrier as the research object. On this basis, the design knowledge is extracted via sensitivity analysis and self-organizing mapping neural network in data mining, and the effectiveness of the rule extraction technology is verified through a comparative analysis of the results. The concluded rules play a significant role in guiding hull form optimization.
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Key words
Multi -objective optimization,Sensitivity analysis,Self -organizing mapping,Extraction of design knowledge
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