Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case
Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case
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DOI：
10.1002/mcda.1605
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发表时间：
2017-01-01
影响因子：
2
通讯作者：
Rigoni, Enrico
中科院分区：
文献类型：
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作者：
Allmendinger, Richard;Emmerich, Michael T. M.;Rigoni, Enrico
解决现实世界中多准则优化问题的复杂性通常源于以下事实：复杂，昂贵和/或耗时的仿真工具或物理实验用于评估问题的解决方案。在这种情况下，通常使用有效的计算模型（通常称为替代物或元模型）来近似模拟或物理实验的结果（客观或约束函数值）。多个目标函数的存在为替代辅助优化带来了额外的复杂性。例如，复杂性可能与适当选择单个目标功能的元模型选择，替代模型的广泛训练时间或多核计算机同时近似多个目标的最佳使用。开箱即用的思考，复杂性也可以从近似单个目标函数转变为近似整个帕累托前部。这导致了进一步的复杂性，即如何在统计上验证并将开发的技术应用于现实世界中的问题。在本文中，我们讨论了与替代辅助的多准则优化中新兴的复杂性相关的主题，这些主题在非辅助辅助单目标优化中可能不普遍。这些复杂性是使用作者参与其中的几个现实世界中的问题。然后，我们讨论了一些有希望的未来研究方向和前瞻性解决方案，以解决替代辅助的多准则优化中新兴的复杂性。最后，我们从工业的角度提供了见解，以了解如何在协作业务环境中开发和应用替代辅助的多标准优化技术来解决现实世界中的问题。
Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization. For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.