Solving complex problems requires continuous effort in developing theory and practice to cope with larger, more difficult scenarios. Working with surrogates is normal for creating a proxy that realistically models the problem into the computer. Thus, the question of how to best define and characterize such a surrogate model is of the utmost importance. In this paper, we introduce the PTME methodology to study deep learning surrogates by analyzing their Precision, Time, Memory, and Energy consumption. We argue that only a combination of numerical and physical performance can lead to a surrogate that is both a trusted scientific substitute for the real problem and an efficient experimental artifact for scalable studies. Here, we propose different surrogates for a real problem in optimally organizing the network of traffic lights in European cities and perform a PTME study on the surrogates' sampling methods, dataset sizes, and resource consumption. We further use the built surrogates in new optimization metaheuristics for decision-making in real cities. We offer better techniques and conclude that the PTME methodology can be used as a guideline for other applications and solvers.

翻译：解决复杂问题需要在理论与实践方面持续努力，以应对规模更大、难度更高的场景。使用代理模型是常见的做法，旨在构建能真实反映问题特征的计算机代理模型。因此，如何最优地定义和表征此类代理模型至关重要。本文提出PTME方法论，通过分析深度学习代理模型的精度、时间、内存及能耗来对其进行研究。我们认为，只有结合数值性能与物理性能，才能构建出既可作为实际问题的可信科学替代，又能作为可扩展研究的高效实验工具的代理模型。本文针对欧洲城市交通信号灯网络优化这一实际问题，提出了不同的代理模型，并对代理模型的采样方法、数据集规模及资源消耗进行了PTME研究。我们进一步将构建的代理模型应用于新的优化元启发式算法，以支持实际城市的决策制定。我们提供了更优的技术方案，并得出结论：PTME方法论可作为其他应用场景与求解器的指导框架。