  In satellite layout design, heat source layout optimization (HSLO) is an
effective technique to decrease the maximum temperature and improve the heat
management of the whole system. Recently, deep learning surrogate assisted HSLO
has been proposed, which learns the mapping from layout to its corresponding
temperature field, so as to substitute the simulation during optimization to
decrease the computational cost largely. However, it faces two main challenges:
1) the neural network surrogate for the certain task is often manually designed
to be complex and requires rich debugging experience, which is challenging for
the designers in the engineering field; 2) existing algorithms for HSLO could
only obtain a near optimal solution in single optimization and are easily
trapped in local optimum. To address the first challenge, considering reducing
the total parameter numbers and ensuring the similar accuracy as well as, a
neural architecture search (NAS) method combined with Feature Pyramid Network
(FPN) framework is developed to realize the purpose of automatically searching
for a small deep learning surrogate model for HSLO. To address the second
challenge, a multimodal neighborhood search based layout optimization algorithm
(MNSLO) is proposed, which could obtain more and better approximate optimal
design schemes simultaneously in single optimization. Finally, two typical
two-dimensional heat conduction optimization problems are utilized to
demonstrate the effectiveness of the proposed method. With the similar
accuracy, NAS finds models with 80% fewer parameters, 64% fewer FLOPs and 36%
faster inference time than the original FPN. Besides, with the assistance of
deep learning surrogate by automatic search, MNSLO could achieve multiple near
optimal design schemes simultaneously to provide more design diversities for
designers.
