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In this paper, a hybrid sunflower-teaching learning-based optimization (SFO-TLBO) algorithm has been proposed, integrating two well-known algorithms, namely sunflower optimization, and teaching–learning-based optimization. The proposed hybrid SFO-TLBO algorithm has been incorporated based on the ratio of the fitness value of the search agent and the average fitness value of the population. The SFO has more tendency of global exploration but has a problem of slow convergence speed. On the other hand, the convergence of the TLBO algorithm is fast, but it may get stuck in local optima. In this work, we have hybridized SFO with TLBO to take advantage of both the algorithms and also to balance the trade-off between exploitation and exploration. The proposed hybrid SFO-TLBO has been first applied to nineteen test benchmark functions. The outcomes have been compared with twelve state-of-the-art algorithms. In seventeen benchmark test functions, better performance has been achieved by the proposed hybrid SFO-TLBO. Also, the proposed hybrid SFO-TLBO algorithm has been used to solve an Internet of Vehicles (IoV) optimization problem and performance has been compared with fifteen state-of-the-art algorithms. In solving the IoV problem, hybrid SFO-TLBO has been achieved better performance in comparison with all fifteen considered algorithms which showcases the applicability of hybrid SFO-TLBO algorithm in resolving large-scale, real-world problems. In addition, for feature selection problems, a binary version of hybrid SFO-TLBO has been proposed, in which a wrapper approach is used to decide an optimal subset of features with a k-nearest neighbor classifier. The proposed binary method also determines an optimal value of k for the k-nearest neighbor classifier. The binary hybrid SFO-TLBO algorithm has been applied to the QSAR biodegradation dataset for classification, and for evidencing its efficiency, its performance has been compared with the other state-of-the-art algorithms. The results of the proposed approach are quite encouraging, and an average accuracy of 89.10% has been achieved. [ABSTRACT FROM AUTHOR]

Some existing feature subset selection algorithms use only one metric, such as symmetric uncertainty, to evaluate redundant features. These algorithms have the problem that some relevant features are considered as redundant and removed. To solve the problem, this paper proposes a feature subset selection algorithm based on equal interval division and three-way interaction information. Symmetric uncertainty between each feature and the class label is first calculated and compared with zero, and irrelevant features are removed. Then, symmetric uncertainty between features is calculated and compared with symmetric uncertainty between features and the class label. The method of equal interval division and ranking is adopted to process symmetric uncertainty between features and the class label as well as symmetric uncertainty between features, and these processed parts are then compared. Three-way interaction information among features and the class label is calculated and compared with zero. The three results of the comparisons are employed to remove redundant features. To validate the performance, the proposed algorithm is compared with several feature subset selection algorithms. Experimental results demonstrate that the proposed algorithm can achieve better feature selection performance. [ABSTRACT FROM AUTHOR]