In this paper, dynamically swarm shared mutation based Bacterial Foraging (DSSBFO) is proposed to optimize multidimensional, unimodal and multimodal functions. In BFO, due to fixed step size it requires more computational cost to get optimum solution with better accuracy. Chemotaxis and reproduction step of BFO are not sufficient for an effective search. So in this paper, the authors propose dynamic step size in BFO to achieve optimum solution with better accuracy with minimum cost. The dynamic step size i.e. mutation is achieved by modifying the position equation of GLBestPSO and momentum factor (mc) of SSMPSO used in modified equation to bring the bacteria in search space and not to cross the boundary of search space. The eight standard benchmark functions are used to prove the performance of DSSBFO in terms of precision and cost. DSSBFO performs well as compared to BFO and BSO (BFO hybridized with PSO) alogrithms interms of quality solution with faster convergence.

本文提出了基于动态群共享变异的细菌觅食算法（DSSBFO），以优化多维，单峰和多峰函数。在BFO中，由于步长固定，因此需要更多的计算成本才能获得具有更好精度的最佳解决方案。BFO的趋化性和生殖步骤不足以进行有效的搜索。因此，在本文中，作者提出了BFO中的动态步长大小，以便以最小的成本获得具有更好精度的最佳解决方案。动态步长（即突变）是通过修改GLBestPSO的位置方程和动量因子（mc）来实现的修正方程式中使用的SSMPSO的特征是将细菌带入搜索空间而不会越过搜索空间的边界。八个标准基准功能用于从精度和成本方面证明DSSBFO的性能。与BFO和BSO（与PSO混合的BFO）算法相比，DSSBFO的性能良好，收敛速度更快。