基于kriging的模拟电路自适应增量学习约束多目标进化算法

S.-D. Yin, Wenfei Hu, Wenyuan Zhang, Ruitao Wang, Jian Zhang, Yan Wang
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引用次数: 3

摘要

本文提出了一种基于kriging的约束多目标进化算法,用于模拟电路的自适应增量学习合成。引入增量学习技术将Kriging模型的训练时间复杂度从$O(n^{3})$降低到$O(n^{2})$,其中$n$为训练点的个数。该方法从三个方面减少了总优化时间。首先,通过重用之前训练好的模型,采用自适应增量学习策略来减少Kriging模型的训练时间。其次,采用非支配排序和修正拥挤距离的方法对最有希望进行模拟的模型进行预筛选,大大减少了模拟次数。第三,由于没有内部优化,节省了Kriging模型的预测时间。在两个现实电路上的实验结果表明,与目前最先进的多目标贝叶斯优化方法相比,我们的方法在不放弃优化结果的情况下,将Kriging模型的训练时间缩短了95%,预测时间缩短了99.7%。与NSGA-II和MOEA/D相比,该方法在总优化时间上的速度提高了10倍,同时取得了更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient Kriging-based Constrained Multi-objective Evolutionary Algorithm for Analog Circuit Synthesis via Self-adaptive Incremental Learning
In this paper, we propose an efficient Kriging-based constrained multi-objective evolutionary algorithm for analog circuit synthesis via self-adaptive incremental learning. The incremental learning technique is introduced to reduce time complexity of training the Kriging model from $O(n^{3})$, to $O(n^{2})$, where $n$ is the number of training points. The proposed approach reduces the total optimization time in three aspects. First, by reusing the previously trained models, a self-adaptive incremental learning strategy is applied to reduce the training time of the Kriging model. Second, we use non-dominated sorting and modified crowding distance to prescreen the most promising one to be simulated, which largely reduce the number of simulations. Third, as there is no internal optimization, the prediction time of the Kriging model is saved. Experimental results on two real-world circuits demonstrate that compared with the state-of-the-art multi-objective Bayesian optimization, our method can reduce the training time of Kriging model by 95% and the prediction time by 99.7% without surrendering optimization results. Compared with NSGA-II and MOEA/D, the proposed method can achieve up to 10X speed up in terms of the total optimization time while achieving better results.
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