Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

The bacterial foraging optimization ( BFO) algorithm can simulate the mechanism of natural selection. However, as the direction of inversion is uncertain in the chemotaxis process, it easily falls into a local optimum. We propose a hybrid algorithm based on simulated annealing (SA) and BFO for mining imbalanced data. The key idea is to exploit the advantages of both SA and the BFO algorithm. In the proposed algorithm, SA finds the optimal solution by employing a jump process, so as to solve the uncertainty of the reversal direction in the chemotaxis process of BFO and avoid falling into a local optimum. SA is used to improve the chemotaxis process of BFO, and then the swarming process, reproduction process, and elimination-dispersal process of BFO are implemented. Four imbalanced datasets are used to test the performance of the proposed hybrid algorithm. In each imbalanced dataset used for testing, there is a certain correlation between the variables, making the dataset multivariate. Through the proposed algorithm, these four multivariate imbalanced datasets are effectively classified, and its performance compared with that of other algorithms. Experimental results show that for the different multivariate imbalanced datasets, the proposed algorithm is better than the original BFO algorithm in terms of various performance indicators. By combining the proposed algorithm with sensor-related technology, in the future, medical multivariate data and security monitoring system data obtained by sensors can be analyzed to improve the classification accuracy of multivariate data.