Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
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DOI：
10.48550/arxiv.2205.14344
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Renzhi Chen;Ke Li
中科院分区：
文献类型：
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作者：
Renzhi Chen;Ke Li
数据驱动的进化多目标优化（EMO）是解决目标函数昂贵的多目标优化问题的一种有效方法。目前的研究主要是开发了一个'定期'三角形的帕累托最优前沿（PF）的问题，而性能可以显着恶化时，PF由断开的部分。此外，在目前的数据驱动的EMO的后代繁殖没有充分利用代理模型的潜在信息。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EMO算法。通过利用最新的代理模型提供的规律性信息，它能够逐步探测一组分布良好的候选解决方案的收敛保证。此外，它的填充准则推荐了一批有前途的候选解决方案进行昂贵的目标函数评估。$33$基准测试问题的实例与断开的PF的实验充分证明了我们所提出的方法对四个选定的同行算法的有效性。
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.