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六种基于种群的连续黑盒优化算法的实验比较。

Experimental comparison of six population-based algorithms for continuous black box optimization.

机构信息

Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic.

出版信息

Evol Comput. 2012 Winter;20(4):483-508. doi: 10.1162/EVCO_a_00083. Epub 2012 Aug 3.

Abstract

Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed comparing continuous optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.

摘要

本文彻底比较了六种基于人群的实值黑箱优化方法。其中一种,Nelder-Mead 单纯形搜索,相当古老,但仍然是直接搜索的流行技术。其余的五种(POEMS、G3PCX、Cauchy EDA、BIPOP-CMA-ES 和 CMA-ES)比较新,来自进化计算领域。最近提出的比较连续优化器(COCO)方法被采用作为比较的基础。结果表明,BIPOP-CMA-ES 达到了最高的成功率,而且通常也相当快。其余算法的结果参差不齐,但 Cauchy EDA 和 POEMS 通常较慢。

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