基于正态云模型的自适应细菌觅食优化算法

The Adaptive Bacteria Foraging Optimization Algorithm Based on Normal Cloud Model

在研究细菌觅食算法趋化、复制、迁徙操作等相关理论的基础上,将云模型和遗传算法相关理论引入,对细菌觅食算法进行优化和改进,在趋化操作中运用X条件云发生器自适应调整细菌灵敏度,控制游动步长,提高了算法的收敛速度;在复制操作中利用遗传算法交叉编译原理,设计交叉算子和遗传算子对算法的复制操作改进,提高算法的局部搜索能力和种群的多样性;在迁徙操作中,利用正向正态云发生器,修正非线性自适应的迁移概率,增强了算法全局寻优能力。最后将改进后的算法应用于自动组卷系统,并与遗传算法进行实验结果比较分析。

On the basis of studying the process of chemotaxis, reproduction, migration theories of Bacteria Foraging Optimization Algorithm, the theory of cloud model and genetic algorithm is introduced into the improvement. Firstly, adjusted by the X-conditional cloud generator for controlling swim steps in the operation of chemotaxis, the convergence rate is improved by this method. Secondly, In the reprodurtion operation, give both the cross operator and mutation operator to improve the local search ability and population diversity, then, in the operation of elimination and dispersal, the adaptive and norrlincar 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 Ucnetic Algorithm.