Developed Optimization Algorithms Based on Natural Taxis Behavior of Bacteria
- PDF / 990,331 Bytes
- 18 Pages / 595.276 x 790.866 pts Page_size
- 20 Downloads / 175 Views
Developed Optimization Algorithms Based on Natural Taxis Behavior of Bacteria Hedieh Sajedi 1 & Fatemeh Mohammadipanah 2 Received: 9 November 2019 / Accepted: 14 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Microbial systems . Bacterial life cycle . Bacterial foraging . Chemotaxis . Nature-inspired . Global optimization . Group intelligence . Metaheuristic algorithm
Introduction Inspiration from nature has been used to solve complex challenges in the past decades. The optimization based on the bacterial life cycle is applied in the subject of metaheuristic search algorithms, swarm intelligence (SI) [1], and the fields of computational intelligence. The metaheuristic is a high-level procedure aimed at the formation or selection of the approach that may enable an acceptable solution for an optimization problem, particularly with incomplete data or partial computation resources.
* Hedieh Sajedi [email protected] * Fatemeh Mohammadipanah [email protected] 1
Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran 14155-6455, Iran
2
Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran 14155-6455, Iran
Optimization algorithms inspired by biological materials have bionic specifications, including the inherent of high error tolerance (in a noisy environment), cross-reproduction or selfreproduction, adaptive self-learning, evolution, and further characteristics [2] [3]. Several natural phenomena, like Darwinian evolution, foraging behavior, and group activities of organisms, can be mimicked for optimization pu
Data Loading...
