通过蒙特卡洛树搜索提升大规模多目标优化的性能不敏感性
Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
AI总结：
本文提出基于蒙特卡洛树搜索的进化算法LMMOCTS，通过采样决策变量构造并筛选优质节点，在提升大规模多目标优化性能的同时降低多次运行的性能波动，并设计两个敏感性指标验证其有效性。
摘要：
大规模多目标优化问题（LSMOP）的特征是同时优化多个相互冲突的目标，并涉及数百个决策变量。工程领域中的许多实际应用都可以建模为LSMOP；同时，工程应用还要求性能具有不敏感性。这一要求通常意味着算法多次运行的结果不仅每次都要在性能上表现良好，而且多次运行的性能不应波动过大，即算法应表现出良好的不敏感性。考虑到每次运行都需要大量计算资源，在提升大规模多目标优化算法性能的同时，改善算法的不敏感性至关重要。然而，现有的大规模多目标优化算法只专注于提升算法性能，而忽略了不敏感性特征。在本工作中，我们提出了一种基于蒙特卡洛树搜索的进化算法用于求解LSMOP，称为LMMOCTS，旨在提升大规模多目标优化问题的性能和不敏感性。所提方法对决策变量进行采样，在蒙特卡洛树上构造新节点以进行优化和评估。它选择评估良好的节点进行进一步搜索，以降低由大规模决策变量引起的性能敏感性。我们将所提算法与几种最先进的设计在不同的基准函数上进行了比较。我们还提出了两个指标来衡量算法的敏感性。实验结果证实了所提设计在求解大规模多目标优化问题方面的有效性和性能不敏感性。
英文摘要：
The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement usually means that the results from the algorithm runs should not only be good for every run in terms of performance but also that the performance of multiple runs should not fluctuate too much, i.e., the algorithm shows good insensitivity. Considering that substantial computational resources are requested for each run, it is essential to improve upon the performance of the large-scale multiobjective optimization algorithm, as well as the insensitivity of the algorithm. However, existing large-scale multiobjective optimization algorithms solely focus on improving the performance of the algorithms, leaving the insensitivity characteristics unattended. In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems. The proposed method samples the decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation. It selects nodes with good evaluation for further search to reduce the performance sensitivity caused by large-scale decision variables. We compare the proposed algorithm with several state-of-the-art designs on different benchmark functions. We also propose two metrics to measure the sensitivity of the algorithm. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving large-scale multiobjective optimization problems.
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