Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20-24, 2023, Proceedings
Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20-24, 2023, Proceedings
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进化多标准优化 - 第 12 届国际会议，EMO 2023，荷兰莱顿，2023 年 3 月 20-24 日，会议记录
DOI：
10.1007/978-3-031-27250-9_5
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Chen R
中科院分区：
文献类型：
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作者：
Chen R
数据驱动的进化多目标优化 (EMO) 已被认为是解决具有昂贵目标函数的多目标优化问题的有效方法。目前的研究主要是针对“规则”三角形帕累托最优前沿（PF）的问题，而当 PF 由断开的线段组成时，性能可能会显着恶化。此外，当前数据驱动的 EMO 中的后代繁殖并没有充分利用替代模型的潜在信息。考虑到这些因素，本文提出了一种基于多重梯度下降的数据驱动的 EMO 算法。通过利用最新代理模型提供的规律性信息，它能够逐步探索一组具有收敛保证的分布良好的候选解决方案。此外，其填充标准推荐了一批有前途的候选解决方案来进行昂贵的目标函数评估。对 33 个具有断开 PF 的基准测试问题实例的实验充分证明了我们提出的方法相对于四种选定的对等算法的有效性。
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a ‘regular’ triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on 33 benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.