一种自适应的模拟细菌觅食算法

针对细菌觅食算法收敛速度慢,存储量大,不能解决高维问题的等缺点,给出了一种自适应的模拟细菌觅食算法.该算法.通过自适应调整细菌的搜索步长,加强了算法在优化初期的全局搜索能力.最后,用5个典型测试函数的实验结果,并与原始细菌觅食算法（BFA）及同样采用了参数调整策略自适应差分进化算法（ADE）和带压缩因子的粒子群算法（YSPSO）进行比较,说明了本文算法的有效性,且其优化能力优于BFA,ADE和YSPSO算法.

An adaptive simulation of bacterial foraging algorithm

To overcome the slow convergence, large store memory capacity and unsuitable to solve the high-dimensi-onal problems of the bacterial foraging algorithm, a novel algorithm was proposed based on the idea of bacterial foraging optimization. A self-adaptive step length is introduced in bacteria to sti ~ngthen the global search ability at the early stage of the proposed algorithm. Finally,the effective- ness and super performance of the proposed algorithm is proved by numerical results of five typical func- tions and comparing the original bacterial foraging algorithm （BFA）,the self-adaptive differential evolu- tion （ADE） and the particle swarm optimization with a compression factor （YSPSO）.