差分進化に基づくセットベース設計手法への有限混合モデルの適用

抄録

In the vehicle development, it is important to solve feasible design sets where all multidisciplinary constraints are satisfied. The set-based design method has been proposed as an effective approach for this purpose. Within this framework, Differential Evolution Based Adaptive Sampling (DEBAS) has been introduced as a technique for exploring feasible regions. However, DEBAS accumulates found feasible solutions, leading to increasing computational costs for calculating the nearest neighbor distance as the number of feasible solutions grows. Consequently, obtaining a sufficient number of feasible solutions within a practical computational time frame can sometimes be difficult. This paper proposes a new method, Differential Evolution Based Adaptive Sampling with Finite Mixture Model (DEBAS-FMM), which applies a finite mixture model to the objective function. This approach aims to mitigate the increase in computational costs associated with the growing number of known feasible solutions, thereby facilitating the derivation of feasible regions more efficiently.