A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems
A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems
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DOI：
10.1109/tevc.2013.2248012
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发表时间：
2014-04-01
影响因子：
14.3
通讯作者：
Gielen, Georges G. E.
中科院分区：
文献类型：
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作者：
Liu, Bo;Zhang, Qingfu;Gielen, Georges G. E.
替代模型辅助进化算法（SAEAS）最近引起了很多关注，因为在许多现实世界中，对计算昂贵的优化的需求日益增长。但是，大多数SAEAS都集中在小规模问题上。 SAEAS用于中等规模的问题（即20-50个决策变量）尚未得到很好的研究。在本文中，提出和研究了中高规模计算昂贵的优化问题（GPEME）的高斯工艺替代模型的辅助进化算法。当高质量的替代模型难以构建和减少维度的技术来应对“维度的诅咒”时，它的主要组成部分是一种替代模型感知的搜索机制，用于昂贵的优化问题。在GPEME中开发和使用了一个新的框架，该框架仔细协调替代建模和进化搜索，以便搜索可以集中在一个小小的有希望的领域上，并得到构建的替代模型的支持。引入了SAMMON映射，以将决策变量从数十个维度转换为几个维度，以便在低维空间中利用高斯工艺替代建模。关于20、30和50个变量的基准问题以及17个变量的现实功率放大器设计自动化问题的实证研究表明，GPEME的效率很高和有效性。与三个最先进的SAEAS相比，可以通过12％至50％的精确功能评估获得更好或类似的解决方案。
Surrogate model assisted evolutionary algorithms (SAEAs) have recently attracted much attention due to the growing need for computationally expensive optimization in many real-world applications. Most current SAEAs, however, focus on small-scale problems. SAEAs for medium-scale problems (i.e., 20-50 decision variables) have not yet been well studied. In this paper, a Gaussian process surrogate model assisted evolutionary algorithm for medium-scale computationally expensive optimization problems (GPEME) is proposed and investigated. Its major components are a surrogate model-aware search mechanism for expensive optimization problems when a high-quality surrogate model is difficult to build and dimension reduction techniques for tackling the "curse of dimensionality." A new framework is developed and used in GPEME, which carefully coordinates the surrogate modeling and the evolutionary search, so that the search can focus on a small promising area and is supported by the constructed surrogate model. Sammon mapping is introduced to transform the decision variables from tens of dimensions to a few dimensions, in order to take advantage of Gaussian process surrogate modeling in a low-dimensional space. Empirical studies on benchmark problems with 20, 30, and 50 variables and a real-world power amplifier design automation problem with 17 variables show the high efficiency and effectiveness of GPEME. Compared to three state-of-the-art SAEAs, better or similar solutions can be obtained with 12% to 50% exact function evaluations.