A Review of Bio Inspired Computing and its Applications
Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.
In Artificial Intelligence swarm intelligence or Bioinspired computation is categorized as a subset. It is classified as fast growing area which was introduced by Gerado Beni and Jing Wang in 1989 in the area of robotic systems. Swarm Intelligence or Bio- Inspired computing can be described as the collective behavior of species available in Nature. Species like social insects as ants, bees and termites are executing the basic rules. The key approach to implement Bio- Inspired computation is problem - solving using nature inspired algorithms. BioInspired computing techniques are adaptable, evolvable, redundant, extendable and innovative. In Bio- Inspired computation the swarm can adjust or self- organize according to the dynamic constraints. Swatting the assets' is a phrase used in many industries and organizations which aim to get as possible values from the existing values. A famous Aristotle quote in support is, ' The whole is more than the sum of the parts'. Every living thing in nature tries to survive according to the natural habitat. Optimal foraging policy is one such phenomenon learned from the living things. By nature all the living things are stochastic behavior. Optimizing the complex values is not an ordinary task. To do this so many algorithms were proposed by some authors. In this research, we are adopting nature-inspired algorithms for optimizing the best results. Nature inspired algorithms are categorized into two categories like swarm based and non- swarm based. When we are discussing about the swarm based algorithms like ant colony optimization, Bee colony, Firefly, glowworm, Lion, Monkey, Bat, Wolf etc.