A Comparison of Global Search Algorithms for Continuous Black Box Optimization
A Comparison of Global Search Algorithms for Continuous Black Box Optimization
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DOI：
10.1162/evco_a_00084
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发表时间：
2012-12-01
影响因子：
6.8
通讯作者：
Pal, Laszlo
中科院分区：
文献类型：
--
作者：
Posik, Petr;Huyer, Waltraud;Pal, Laszlo
描述了四种起源于数学规划界的全局数值黑箱优化方法，并与最先进的进化方法BIPOP-CMA-ES进行了实验比较。选择用于比较的方法展示了进化计算社区可能感兴趣的各种特征：搜索空间的系统采样（DIRECT， MCS）可能与局部搜索方法（MCS）相结合，或者多启动方法（NEWUOA， GLOBAL）可能配备了仔细选择的点，以便从（GLOBAL）运行局部优化器。采用最近提出的“比较连续优化器”（COCO）方法作为比较的基础。在此基础上，我们根据功能评估的可用预算提出了应该使用哪种算法的建议，并提出了几种混合进化算法（EAs）与其他比较算法特征的可能性。
Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed "comparing continuous optimizers" (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms.