An Adaptive Bacterial Foraging Optimization Algorithm Mixed with Bee Colony Algorithm

The Bacterial Foraging OptimizationAlgorithm(BFOA) has poor global search ability and is easily trapped into local opti- mum.In order to solve these problems, an adaptive hybrid BFOA fused with Artificial Bee Colony(ABC) algorithm is proposed. Firstly, Employed Bees Style Chemotaxis(EC) is proposed, which greatly enhances the algorithm's capability of global searching. Then the original fixed step size chemotaxis is changed into an adaptive step size one, which improves the solution precision. On the basis of above, an evaluation method for diversity is put forward to switch two chemotaxis automatically. In order to overcome degradation of diversity caused by direct copy, a copy method based on t-distribution disturbance is proposed. A scout bees style migration based on opposition-based learning is put forward to avoid premature. Simulation experimental results show that the proposed algorithm has a better performance in terms of optimization ability,convergence speed and population diversity compared with ABC algorithm and BFOA.