Batched Data-Driven Evolutionary Multiobjective Optimization Based on Manifold Interpolation
Batched Data-Driven Evolutionary Multiobjective Optimization Based on Manifold Interpolation
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DOI：
10.1109/tevc.2022.3162993
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发表时间：
2021-09
影响因子：
14.3
通讯作者：
Ke Li;Renzhi Chen
中科院分区：
文献类型：
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作者：
Ke Li;Renzhi Chen
多目标优化问题在现实世界的科学、工程和设计优化问题中无处不在。目标函数是一个黑箱的情况并不少见，其评估通常涉及耗时和/或昂贵的物理实验。数据驱动的进化优化可以用来搜索一组非支配的折衷解决方案，其中昂贵的目标函数近似为代理模型。在这篇文章中，我们提出了一个实现批量数据驱动的进化多目标优化（EMO）的框架。它是如此的通用，以至于任何现成的EMO算法都可以以插件的方式应用。有两个独特的组成部分：1）基于Karush-Kuhn-Tucker条件，流形插值方法，探索更多样化的解决方案，并沿着近似帕累托最优集的流形沿着收敛保证; 2）批量推荐方法，通过并行评估多个样本，减少数据驱动的进化优化过程的计算时间。通过对168个具有不同性质的基准测试问题实例的实验和超参数优化的实际应用，与现有的7种代理辅助进化算法进行了比较，充分证明了该框架的有效性和优越性，具有更快的收敛速度和对各种Pareto最优前沿形状更强的适应能力.
Multiobjective optimization problems are ubiquitous in real-world science, engineering, and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of nondominated tradeoff solutions, where the expensive objective functions are approximated as a surrogate model. In this article, we propose a framework for implementing batched data-driven evolutionary multiobjective optimization (EMO). It is so general that any off-the-shelf EMO algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush–Kuhn–Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against seven state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various Pareto-optimal front shapes.