Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture the correlation between data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization exercises. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a strictly limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm against nine state-of-the-art peer algorithms.
翻译:许多现实世界的问题通常是计算成本高,而客观功能则随着时间而演变。数据驱动的,a.k.a. 代理辅助的,进化优化被公认为是在静态环境中解决昂贵黑盒优化问题的有效方法,而在动态环境中却很少加以研究。本文件提出一个简单而有效的转移学习框架,以赋予数据驱动的进化优化解决动态优化问题的权力。具体地说,它应用一个等级化的多输出高斯进程来捕捉从不同时间步骤中收集的数据与从线性上增加的超参数之间的关联。此外,还提议了适应性源任务选择,以及一个清晰的温暖的初始化机制,以更好地利用从以往优化工作中获取的知识。通过这样做,数据驱动的进化优化可以在新的环境中以严格有限的计算预算启动优化。关于合成基准测试问题的实验和现实世界案例研究表明我们提议的算法相对于九种最先进的同行算法的有效性。