An Overview on Nature-Inspired Optimization Algorithms and Their Possible Application in Image Processing Domain
Krishna Gopal Dhal, Arunita Das, Jorge Gálvez, Swarnajit Ray, Sanjoy Das
Abstract
Nature-inspired optimization algorithms are simple and effective tools for solving highly non-linear and multi-modal real-world problems. Nowadays this kind of methods is very attractive for researchers in different fields of science and engineering. The aim of this article is to provide a review that includes the general implementation view of nature-inspired algorithms, the brief over view of random walks, improvement strategies of NIOAs, efficiency comparison techniques and so on. Selection and development of NIOAs are the main challenges to the researchers. Therefore, this paper also includes the recently developed popular NIOAs depending on citation metric and presents their working strategies in brief to meet the main thrust of this domain. At last, the study also briefly discusses the possible applications of NIOAs over digital image processing domain.