针对细菌觅食优化算法经常出现的速度较慢、步长一致的缺陷,赋予细菌灵敏度的概念,对细菌游动的步长进行调节以提高收敛速度.采用免疫算法中的克隆选择思想,对精英细菌群体进行克隆、高频变异和随机交叉,引导算法提高搜索精度.典型高维函数测试表明,改进算法的搜索速度和精度得到极大提升,算法改造后可适用于多维、约束等实际工程问题中的优化.%To correct the defects such as slower speed, step consistence in bacteria foraging optimization algorithm, this paper presented the concept of the sensitivity of bacteria to increase convergence speed by adjusting the step size of bacterial swimming. The clonal selection ideas in immune algorithm were used to achieve bacterial cloning, high-frequency variation and random crossover of the elite group, and to guide the search algorithm to improve accuracy. A number of typical high-dimensional function tests show that the improved algorithm has been greatly improved in terms of search speed and accuracy, and is more appropriate to solve practical engineering optimization problems such as high dimensionality, constraints.
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