School of Chemistry, Damghan University of Basic Sciences, Damghan, Iran.
Anal Chim Acta. 2010 Mar 17;663(1):7-10. doi: 10.1016/j.aca.2010.01.024. Epub 2010 Jan 18.
Ant colony optimization (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. In this paper, ACO algorithm is proposed to feature selection in quantitative structure property relationship (QSPR) modeling and to predict lambda(max) of 1,4-naphthoquinone derivatives. Feature selection is the most important step in classification and regression systems. The performance of the proposed algorithm (ACO) is compared with that of a stepwise regression, genetic algorithm and simulated annealing methods. The average absolute relative deviation in this QSPR study using ACO, stepwise regression, genetic algorithm and simulated annealing using multiple linear regression method for calibration and prediction sets were 5.0%, 3.4% and 6.8%, 6.1% and 5.1%, 8.6% and 6.0%, 5.7%, respectively. It has been demonstrated that the ACO is a useful tool for feature selection with nice performance.
蚁群优化（ACO）是一种元启发式算法，它源自对真实蚂蚁的观察。在本文中，提出了蚁群算法来进行定量结构-性质关系（QSPR）建模中的特征选择，并预测 1,4-萘醌衍生物的λ（max）。特征选择是分类和回归系统中最重要的步骤。将提出的算法（ACO）的性能与逐步回归、遗传算法和模拟退火方法进行了比较。在使用蚁群算法、逐步回归、遗传算法和模拟退火的 QSPR 研究中，使用多元线性回归方法对校准集和预测集的平均绝对相对偏差分别为 5.0%、3.4%和 6.8%、6.1%和 5.1%、8.6%和 6.0%、5.7%。结果表明，ACO 是一种有用的特征选择工具，具有良好的性能。
