A Surrogate-Based Optimization Method with Dynamic Adaptation for High-Dimensional Mixed-Integer Problems
A Surrogate-Based Optimization Method with Dynamic Adaptation for High-Dimensional Mixed-Integer Problems
复制标题
一种基于代理的动态自适应优化方法解决高维混合整数问题
DOI：
10.1016/j.swevo.2022.101099
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发表时间：
2022-05
影响因子：
10
通讯作者：
Xuekai Cen
中科院分区：
文献类型：
--
作者：
Liang Zheng;Youpeng Yang;Guanqi Fu;Zhen Tan;Xuekai Cen
·提出SODA-AD及其两个变体以在有限的计算资源下解决HMIO-B。SODA-AD及其两个变体可以很好地平衡全局和局部搜索。· SODA-AD及其两种变体优于其他同类算法。本文提出了一种基于代理的全维扰动搜索动态适应优化算法（SODA-AD），用于求解具有黑箱目标函数的高维混合整数优化问题（HMIO-B）。SODA-AD以两种方式改进了动态坐标搜索（DCS），即，提出了一种新的候选点采样方法和一种新的自适应缩放方法，有助于平衡全局和局部搜索，解决高维问题。此外，描述了SODA-AD的两种变体。一种是SODA-AD与改进的填充策略（SODA-ADM），它使用预测评分标准，以取代加权评分标准时，预算的计算资源将耗尽。第二种方法首先采用SODA-ADM，然后对每一次迭代周期性地进行序贯维扰动搜索，以继续局部搜索，称为SODA-ADM-DP。在数值实验中，我们使用一个复杂的现实世界的工程问题和八个100维（100-D）的基准问题，比较SODA-AD和它的两个变种与其他知名同行。结果表明，SODA-AD及其两个变体在大多数测试问题上的性能优于其他同类算法，有望用于求解具有黑箱目标函数或非线性非凸目标函数的高维混合整数优化问题.
• SODA-AD and its two variants are proposed to address HMIO-B under limited computational resources. • SODA-AD and its two variants can well balance global and local search. • SODA-AD and its two variants outperform other counterpart algorithms. This study develops a Surrogate-based Optimization algorithm with Dynamic Adaptation of perturbation search of All Dimensions (SODA-AD) to address high-dimensional mixed-integer optimization problems with a black-box objective function (HMIO-B). SODA-AD improves dynamic coordinate search (DCS) in two ways, i.e., with a new way of sampling candidate points and a new adaptive scaling method, which help to balance global and local search and address high-dimensional problems. Additionally, two variants of SODA-AD are described. One is SODA-AD with a modified infill strategy (SODA-ADM), which uses the prediction scoring criterion to replace the weighted scoring criterion when the budgeted computation resources are going to run out. The second method first employs SODA-ADM and then carries out a sequential dimensioned perturbation search for each iteration periodically to continue the local search, and this is named SODA-ADM-DP. In numerical experiments, we compare SODA-AD and its two variants with other well-known counterparts using one complex real-world engineering problem and eight 100-dimensional (100-D) benchmark problems. It is concluded that SODA-AD and its two variants outperform the other counterparts on most of the test problems and are promising for solving high-dimensional mixed-integer optimization problems with black-box objective functions or nonlinear and nonconvex objective functions.
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影响因子：
14.3
作者：
R. Regis;C. Shoemaker
通讯作者：
R. Regis;C. Shoemaker
影响因子：
0.9
作者：
Kenny Q. Ye;William Li;A. Sudjianto
通讯作者：
Kenny Q. Ye;William Li;A. Sudjianto
影响因子：
14.3
作者：
Liu, Bo;Zhang, Qingfu;Gielen, Georges G. E.
通讯作者：
Gielen, Georges G. E.
DOI：
10.2514/6.2004-4457
发表时间：
2004-08
期刊：
--
影响因子：
--
作者：
M. Eldred;A. Giunta;S. S. Collis-S.
通讯作者：
M. Eldred;A. Giunta;S. S. Collis-S.
影响因子：
4.2
作者：
Kleijnen, Jack P. C.
通讯作者：
Kleijnen, Jack P. C.