基于云模型的细菌觅食优化算法

针对细菌觅食优化算法收敛速度慢、容易陷入局部极值点出现早熟的问题,提出一种新的基于云模型优化的细菌觅食优化算法.首先给出了细菌灵敏度的概念,结合云模型随机性和稳定倾向性的特点,运用了X条件云发生器来调整细菌灵敏度,控制游动步长,进行了趋向性操作和复制操作,改进了标准的细菌觅食优化算法,提高了算法的收敛速度.然后利用正向正态云发生器,修正非线性自适应的迁移概率,进行了迁移操作,增强了算法的全局寻优能力.将该算法应用于自动组卷系统中,与遗传算法进行实验比较分析,结果表明:该算法的收敛速度与优化质量均优于遗传算法.

The Bacteria Foraging Optimization Algorithm Based on the Cloud Model

A new bacteria foraging optimization algorithm based on the cloud model is presented for solving the problems of slow convergence rate,partial optimum and premature convergence.Firstly,in the operation of chemotaxis and reproduction,the conception of sensitivity is given and adjusted by the X-conditional cloud generator for controlling swim steps,combined with the characters of randomness and stability of the cloud model.The convergence rate is improved by this method.Then,in the operation of elimination and dispersal,the adaptive and non-linear probability of elimination and dispersal is adopted by the forward normal cloud generator,which improves the global-optimization capability.Finally,this algorithm is used to the system of automatic test,compared and analyzed with the experiment of Genetic Algorithm.The results of experiment show that this algorithm is better than Genetic Algorithm both in convergence rate and quality of optimization.