A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
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DOI：
10.1109/cec.2008.4631330
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发表时间：
2008-06
期刊：
影响因子：
--
通讯作者：
Yu Wang;Bin Li
中科院分区：
文献类型：
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作者：
Yu Wang;Bin Li
本文提出了一种用于大规模全局优化（LSGO）问题的单变量 EDA，表示为 ldquoLSEDA-glrdquo。采用混合高斯和Levy概率分布下采样、标准差控制策略和重启策略三种有效策略来提高经典单变量EDA在LSGO问题上的性能。此类工作的动机是将 EDA 合理地扩展到 LSGO 领域。 LSEDA-gl、仅采用标准偏差控制策略的 EDA (EDA-STDC) 和类似 EDA 版本“连续单变量边缘分布算法”UMDAc 之间的比较是在经典测试函数上进行的。基于通用比较标准，讨论了算法的优缺点。此外，LSEDA-gl还在CECpsila2008 LSGO特别会议上提供的7个维度为100、500、1000的函数上进行了测试。这项工作也有望为 CECpsila2008 特别会议提供比较结果。
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.