An effective bacterial foraging optimizer for global optimization

摘要: An effective bacterial foraging optimizer for global optimization is presented.The utilizes combination of gravitational search and swarm diversity strategies.The algorithm used to solve various types benchmark functions identify parameters a chaotic system.The results are compared with the its competitors presented in literatures. Bacterial (BFO) inspired by behavior bacteria called chemotaxis novel stochastic algorithm, chemotactic movement mimics trial solution through random directions. However, it may enable BFO possess poor optimizing performance as other methods over complex problems. To improve exploration exploitation abilities standard BFO, this paper proposes an (EBFO). First strategy incorporated into step adjust unit length according information. Then, integrated reproduction enhance mode depending on diversity. We evaluate EBFO 23 numerical functions, then applied identifying system. The simulation show that proposed more than can be extended