Bitterling fish optimization (BFO) algorithm

Abstract

The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN.