A Design Optimization Method with Sparse Scattered Data and Evolutionary Computation

Abstract

Engineering design can be regarded as an iterative optimization process. This process is difficult because of two main problems: the first is that computer-aided engineering (CAE) is time-consuming in terms of evaluating design solutions, while the second is the high dimensionality of design solutions. In the research community, a surrogate model is proposed to deal with the first problem while an evolutionary algorithm is adopted for the second. In this work, we develop a new method with only sparse scattered data, which is very common in many practical scenarios. The surrogate model can also assign a penalty factor for the predicted value, and this penalty factor can be used as one of the targets of the evolutionary algorithm to balance global exploration and local exploit. We also adopt a new evolutionary strategy, which can search high-dimensional space. Three groups of experiments are conducted to validate the proposed methods. The experimental results show that the surrogate model can predict performance and the corresponding penalty factor, the evolutionary strategy is better in terms of searching high-dimensional space compared with other evolutionary strategies, and the whole method can generate new design solutions that are near to the known design solutions. The experimental results show that this method can be used in practical scenarios, especially where they only have sparse scattered data.