Deep generative model for non-convex constraint handling
Deep generative model for non-convex constraint handling
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DOI：
10.1145/3377930.3390170
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Naoki Sakamoto;Eiji Semmatsu;Kazuto Fukuchi;Jun Sakuma;Youhei Akimoto
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文献类型：
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作者：
Naoki Sakamoto;Eiji Semmatsu;Kazuto Fukuchi;Jun Sakuma;Youhei Akimoto
在本研究中，我们考虑非凸约束的黑盒最小化问题，其中约束的评估成本明显低于目标。非凸约束通常使使用进化方法解决问题变得困难。在本文中，我们重新讨论了一种称为解码器约束处理的传统技术，该技术将可行的非凸域转换为易于控制的凸集。这种方法很有前途，因为它把一个有约束的问题变成了一个几乎没有约束的问题。然而，由于设计或训练这样的非线性解码器需要领域知识或人工准备的训练数据，其应用受到相当大的限制。为了使解码器设计完全自动化，我们使用了深度生成模型。我们提出了一种新的方案来训练深度生成模型，而不使用人工准备的训练数据。为此，我们首先使用约束函数训练可行解采样器，即深度神经网络。随后，我们使用训练样本生成的数据作为训练数据来训练另一个深度生成模型。该框架适用于由拓扑优化问题引发的任务。实证研究表明，与现有方法相比，该方法可以在较少的目标函数评价下找到更好的解。
In this study, we consider black-box minimization problems with non-convex constraints, where the constraints are significantly cheaper to evaluate than the objective. Non-convex constraints generally make it difficult to solve problems using evolutionary approaches. In this paper, we revisit a conventional technique called decoder constraint handling, which transforms a feasible non-convex domain into an easy-to-control convex set. This approach is promising because it transforms a constrained problem into an almost unconstrained one. However, its application has been considerably limited, because designing or training such a nonlinear decoder requires domain knowledge or manually prepared training data. To fully automate the decoder design, we use deep generative models. We propose a novel scheme to train a deep generative model without using manually prepared training data. For this purpose, we first train feasible solution samplers, which are deep neural networks, using the constraint functions. Subsequently, we train another deep generative model using the data generated from the trained samplers as the training data. The proposed framework is applied to tasks inspired by topology optimization problems. The empirical study demonstrates that the proposed approach can locate better solutions with fewer objective function evaluations than the existing approach.