Developed Optimization Algorithms Based on Natural Taxis Behavior of Bacteria

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.