An effective bacterial foraging optimizer for global optimization

Bacterial foraging optimization (BFO) inspired by a behavior of bacteria called chemotaxis is a novel stochastic optimization algorithm, its chemotactic movement mimics a trial solution through random search directions. However, it may enable BFO to possess a poor optimizing performance as compared to other optimization methods over complex optimization problems. To improve the exploration and exploitation abilities of the standard BFO, this paper proposes an effective bacterial foraging optimization (EBFO). First a gravitational search strategy is incorporated into the chemotaxis step to adjust its unit length according to the swarm information. Then, a swarm diversity strategy is integrated into the reproduction step to enhance the reproduction mode depending on the swarm diversity. We evaluate the performance of the EBFO on 23 numerical benchmark functions, then it is applied to identifying parameters of a chaotic system. The simulation results show that the proposed algorithm is more effective than its competitors and can be extended to other global optimization problems. (C) 2015 Elsevier Inc. All rights reserved.