树状高斯过程回归求解离线数据驱动的连续多目标优化问题。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Atanu Mazumdar, Manuel López-Ibáñez, Tinkle Chugh, Jussi Hakanen, Kaisa Miettinen
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引用次数: 0

摘要

对于离线数据驱动的多目标优化问题(MOPs),在优化过程中没有新数据可用。首先使用提供的离线数据构建近似模型(或代理),然后使用优化器(例如,多目标进化算法)以代理作为目标函数来寻找问题的帕累托最优解。与在线数据驱动的MOPs相比,这些代理不能随着新数据的更新而更新,因此在优化过程中不能通过考虑新数据来提高近似精度。高斯过程回归(GPR)模型由于能够提供不确定性信息而被广泛用作替代方法。然而,当数据集的大小很大时,构建gpr在计算上变得非常昂贵。使用稀疏GPRs减少了构建代理的计算成本。然而,稀疏gpr并不适合解决离线数据驱动的MOPs,在这种情况下,需要在Pareto最优解附近有良好的代理精度。本文提出了具有连续决策变量的离线数据驱动MOPs的树状GPR (TGPR-MO)替代算法。该方法首先利用回归树将决策空间划分为子区域,并在决策空间中接近Pareto最优解的区域依次构建gpr,以准确地近似目标函数之间的权衡。TGPR-MO替代品的计算成本较低,因为gpr仅在决策空间的较小区域中利用数据子集构建。TGPR-MO替代物在具有不同数据大小、采样策略、目标函数数量和决策变量的基于距离的可视化问题上进行了测试。实验结果表明,TGPR-MO替代算法计算成本低,可以处理大数据集。此外,与全gpr和稀疏gpr相比,TGPR-MO替代品产生的解更接近帕累托最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems.

For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for example, a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.

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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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