Bio-inspired optimization algorithms are capable of resolving a wide variety of challenges in science and technology, including cognitive science. The principles used by the smallest living organisms in the world could be adopted in the decision-based algorithms for artificial intelligence purposes. Bacterial biological functions and behaviors have been the most effective strategies, which have evolved in these single-cell organisms. The bacteria live based on cognitive and social sensing in nature. Using cognitive processing in bacterial populations enables them to perceive the dynamic surrounding ecosystem and explore their environment. Recently, the behavioral pattern of bacterial foraging has been recruited for resolving optimization issues. This paper reviews 22 developed optimization algorithms based on the bacterial life cycle of motile bacteria. The solicitation of these algorithms applies to a wide range of topics, including cognitive analysis, engineering, medicine, and industry. Following a comparison between different algorithms, we summarize the application of the algorithms in these areas. Eventually, some points are suggested for developing and employing the algorithms in future practical applications of cognitive technology.

受生物启发的优化算法能够解决包括认知科学在内的科学技术的各种挑战。出于人工智能目的，基于决策的算法中可以采用世界上最小的生物体使用的原理。细菌的生物学功能和行为一直是最有效的策略，这些策略已在这些单细胞生物中得到发展。这种细菌是基于自然界的认知和社会感知而生活的。在细菌种群中使用认知处理使他们能够感知周围的动态生态系统并探索其环境。最近，已经招募了细菌觅食的行为模式以解决优化问题。本文回顾了根据运动细菌的生命周期开发的22种优化算法。这些算法的征求涉及广泛的主题，包括认知分析，工程，医学和工业。在对不同算法进行比较之后，我们总结了这些算法在这些领域中的应用。最终，提出了在认知技术的未来实际应用中开发和使用算法的一些观点。