一种具有量子行为的细菌觅食优化算法

为改善细菌觅食优化(BFO)算法中群体信息共享机制，增强算法的全局搜索性能，该文将细菌个体放在量子空间中描述，根据细菌群体信息建立量子化的势能阱模型，通过蒙特卡洛随机采样完成繁殖操作，使得细菌群能对整个空间进行搜索。针对BFO算法中趋化步长一致的缺陷，该文提出了一种动态缩进控制策略，在保证算法收敛性的同时大大增加了个体全局寻优的几率。标准测试函数的仿真结果表明，所提出算法具有精度高、成功率大、全局寻优性能强的特点。

Bacterial Foraging Optimization Algorithm with Quantum Behavior

In order to enhance the global optimization capability and quorum sensing mechanism of Bacterial Foraging Optimization (BFO) algorithm, a novel Bacterial Foraging Optimization algorithm with Quantum Behavior (QBFO) is proposed. In this method, the bacteria individual is described in the quantum space and a potential well model is created. Using Monte Carlo method to achieve the reproduction of bacterial swarming, and which makes the population are able to search the whole space. In view of the defects of the fixed swim step in bacterial foraging algorithm, a dynamic indented control strategy is introduced in this paper, which ensures the convergence of algorithm and increases the possibility of exploring a global optimum. The experiment results on classic functions demonstrate the global convergence ability of the proposed method with better accuracy and more probability of finding global optimum.