Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

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 λ_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.