Adaptive Hybrid Bacterial Foraging Optimization Algorithm Based on Cloud Theory

Traditional bacteria foraging optimization algorithm exists some shortcomings, such as large storage, slow convergence speed and difficult to solve the high-dimensional problem, so an adaptive hybrid bacterial foraging optimization algorithm based on cloud theory is proposed in this paper. The bacterial are divided into three levels based on the fitness of bacterial, and different level adopted search step length. The search step length in common level is adaptively varied depending on X-conditional cloud generator. And the thought of PSO was brought into the new algorithm to update bacteria's position. The new algorithm's flexibility would be better because of the stable tendency and randomness property of the cloud model. The introduction of PSO improved global searching ability and sped up the convergence speed of the algorithm. Experiments show that the new algorithm(CAHBFO) is superior than BFO、NBFO and YSPSO in dealing with high-dimensional problems、convergence speed and calculation accuracy.