The Bitterling Fish Optimization (BFO) algorithm is a meta-heuristic method inspired by the mating behavior of bitterling fish. This algorithm is designed to solve optimization problems by simulating the fish's search for suitable oysters and mating strategies. The BFO algorithm outperforms several existing meta-heuristic algorithms, including the Gray Wolf Optimization (GWO), Whale Optimization (WOA), and Harris Hawks Optimization (HHO), in terms of accuracy and efficiency on benchmark functions and real-world applications. The algorithm is applied to various domains, including data mining, machine learning, and network intrusion detection. In data mining and machine learning, the BFO algorithm is used for feature selection and intrusion detection. When combined with an MLP artificial neural network, the BFO algorithm achieves high accuracy in detecting network intrusions, with an accuracy of 99.14%, precision of 98.87%, and sensitivity of 98.85% on the NSL KDD dataset. The BFO algorithm is also used for feature selection in the UNSW-NB15 dataset, achieving an accuracy of 96.72%. Additionally, the BFO algorithm is applied to improve K-means clustering, outperforming methods like iECA*, ECA*, and GENCLUST++ in clustering tasks related to diseases such as COVID-19, diabetes, and kidney disease. The BFO algorithm is also used in phishing attack detection and disease detection. When combined with an artificial neural network, the BFO algorithm achieves high accuracy in detecting phishing attacks. The algorithm is also used for clustering, where it outperforms other clustering methods in terms of accuracy and efficiency. The BFO algorithm is evaluated on various benchmark functions and real-world applications. It is compared with other meta-heuristic algorithms, such as GWO, WOA, and HHO, in terms of convergence, stability, and execution time. The BFO algorithm shows superior performance in terms of accuracy, convergence, and stability, with a lower error rate and faster execution time compared to other algorithms. The BFO algorithm is also used in network intrusion detection, where it is combined with an MLP neural network to detect network intrusions. The algorithm is evaluated on the NSL KDD dataset, achieving high accuracy in detecting network intrusions. The BFO algorithm is also used in phishing attack detection, where it is combined with an artificial neural network to detect phishing attacks. The BFO algorithm is a promising meta-heuristic method that can be applied to various optimization problems. It is efficient, accurate, and has a low error rate, making it suitable for use in data mining, machine learning, and network intrusion detection. The algorithm is also used in clustering, where it outperforms other clustering methods in terms of accuracy and efficiency. The BFO algorithm is a valuable tool for solving optimization problems in various domains.The Bitterling Fish Optimization (BFO) algorithm is a meta-heuristic method inspired by the mating behavior of bitterling fish. This algorithm is designed to solve optimization problems by simulating the fish's search for suitable oysters and mating strategies. The BFO algorithm outperforms several existing meta-heuristic algorithms, including the Gray Wolf Optimization (GWO), Whale Optimization (WOA), and Harris Hawks Optimization (HHO), in terms of accuracy and efficiency on benchmark functions and real-world applications. The algorithm is applied to various domains, including data mining, machine learning, and network intrusion detection. In data mining and machine learning, the BFO algorithm is used for feature selection and intrusion detection. When combined with an MLP artificial neural network, the BFO algorithm achieves high accuracy in detecting network intrusions, with an accuracy of 99.14%, precision of 98.87%, and sensitivity of 98.85% on the NSL KDD dataset. The BFO algorithm is also used for feature selection in the UNSW-NB15 dataset, achieving an accuracy of 96.72%. Additionally, the BFO algorithm is applied to improve K-means clustering, outperforming methods like iECA*, ECA*, and GENCLUST++ in clustering tasks related to diseases such as COVID-19, diabetes, and kidney disease. The BFO algorithm is also used in phishing attack detection and disease detection. When combined with an artificial neural network, the BFO algorithm achieves high accuracy in detecting phishing attacks. The algorithm is also used for clustering, where it outperforms other clustering methods in terms of accuracy and efficiency. The BFO algorithm is evaluated on various benchmark functions and real-world applications. It is compared with other meta-heuristic algorithms, such as GWO, WOA, and HHO, in terms of convergence, stability, and execution time. The BFO algorithm shows superior performance in terms of accuracy, convergence, and stability, with a lower error rate and faster execution time compared to other algorithms. The BFO algorithm is also used in network intrusion detection, where it is combined with an MLP neural network to detect network intrusions. The algorithm is evaluated on the NSL KDD dataset, achieving high accuracy in detecting network intrusions. The BFO algorithm is also used in phishing attack detection, where it is combined with an artificial neural network to detect phishing attacks. The BFO algorithm is a promising meta-heuristic method that can be applied to various optimization problems. It is efficient, accurate, and has a low error rate, making it suitable for use in data mining, machine learning, and network intrusion detection. The algorithm is also used in clustering, where it outperforms other clustering methods in terms of accuracy and efficiency. The BFO algorithm is a valuable tool for solving optimization problems in various domains.