This paper proposes a scheme of the grouping Monte Carlo tree search for the optimization problems with broad branches. By classifying nodes which represent candidates of adjacent geometric parameters, the search goes through selecting and backpropagating based on the information of the groups, instead of nodes. Thus, the number of searching times can be reduced dramatically to traverse down leaf nodes, while ensuring the accuracy of the optimal solution. We have used it to successfully design a three-dimensional (3D) inductor.
BrowneCBPowleyEWhitehouseD, et al.A survey of Monte Carlo tree search methods. IEEE Trans Comput Intell AI Games2012 Feb; 4: 1–43.
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SatoHIgarashiH. Automatic design of PM motor using Monte Carlo tree search in conjunction with topology optimization. IEEE Trans Magn2022 Sept; 58: 1–4.
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SatoHIgarashiH. Multi-objective automatic design of permanent magnet motor using Monte Carlo tree search. IEEE Trans Magn2023 May; 59: 1–4.
