Bio inspired computing – A review of algorithms and scope of applications

Abstract

With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.