An improved neural network algorithm based on artificial bee colony algorithm and its application in sewage treatment

As an advanced artificial intelligence technology, error back-propagation (BP) neural network algorithm has been widely applied to electronics, communications, automation and other fields. However, traditional BP neural network algorithm has the disadvantages, such as inclination to stick into local optima, and slow convergence, which exert a great impact on the processing performance, and also limit its further application. Artificial bee colony (ABC) algorithm has been developed as a new heuristic algorithm in recent years based on foraging behavior, which is also proposed to be a global optimization algorithm. As a simple and reliable algorithm, ABC algorithm is easy to be implemented. In this paper, an optimized BP neural network algorithm based on ABC algorithm is proposed so as to integrate the advantages of both algorithms. Simulation result shows that compared with the conventional BP neural network algorithm, the new algorithm has gained a significant development not only in prediction accuracy but also in convergence speed.