Nature inspired optimization algorithms: a comprehensive overview

Nature performs complex tasks in a simple yet efficient way. Natural processes may seem straightforward from outside but are composed of several inherently complicated sub-processes. Inspired from nature, several Nature Inspired Optimization Algorithms (NIOAs) have been developed in recent years. The family of NIOAs is expanding rapidly. Therefore, the set of NIOAs became quite large and selecting an appropriate NIOA is a tedious job. Since each one of the algorithms offers something novel, the similarities and differences among them are necessary to be established so that the selection of an algorithm for a particular problem becomes relatively easy. Moreover, a problem needs to be mapped in a NIOA, requiring understanding of fundamental components of NIOAs. Tuning parameters and algorithm operators another important concern in NIOAs that need be addressed carefully for better performance of the algorithm. Our work distinguishes NIOAs on the basis of various criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. The purpose of present study is to analyze major concepts related to NIOAs such as fundamentals of NIOAs, comparison among them, advancements, etc. In order to explain the usage of components of NIOA, an illustrative example is also presented.