Gravitation field algorithm and its application in gene cluster
Gravitation field algorithm and its application in gene cluster
复制标题
引力场算法及其在基因簇中的应用
DOI：
10.1186/1748-7188-5-32
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发表时间：
2010-09-20
影响因子：
1
通讯作者：
Wang, Yan
中科院分区：
文献类型：
--
作者：
Zheng, Ming;Liu, Gui-xia;Wang, Yan
背景在从现有的实验数据或给定的功能中进行基因聚类时，搜索最优解是最具挑战性的任务之一。蚁群算法、遗传算法、粒子群算法等类似的高效全局优化方法被生物技术工作者广泛使用。这些算法都是基于对自然现象的模拟。结果提出了一种新的搜索优化算法--引力场算法(GFA)，该算法源于著名的天文学理论--行星形成的太阳星云模型。GFA模拟了引力场，在一些多峰函数优化问题上优于GA和SA。GFA也可以以单峰函数的形式使用。结论数学证明表明，对于一个独立变量质量函数，GFA在三种情况下都能以概率1收敛于全局最优解。除了这些结果外，本文还利用本文的基本优化概念分析了SA和GA对全局搜索的影响以及SA和GA的固有缺陷。一些结果和源代码(MatLab)可以在http://ccst.jlu.edu.cn/CSBG/GFA上公开获得。
BackgroundSearching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena.ResultsThis paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus.ConclusionsThe mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA .