Nature Inspired Optimizers and Their Importance for AI: An Inclusive Analysis

A group of algorithms known as Nature Inspired Optimizers (NIO) are inspired by how things behave in the natural world. Animal actions, biological processes, chemical reactions, etc., have all served as inspiration for NIOs. Natural methods are easily divided into numerous intricate subprocesses. Because of this, every algorithm is distinct and potent. By addressing problems with selection of algorithms, parameter tuning/modification, and updating problems, the learning of NIOs aims to expand the performance of nature-inspired algorithms. Natural occurrences are constantly changing, and eventually, an algorithm’s behavior must also change. As a result, we must continually discover and modify existing ones. The genetic algorithm, Artificial Bee Colony Algorithm (ABCA), Bat Algorithm (BA), Grey Wolf Optimizer, and others are samples of NIOs. An in-depth study of various NIOs and their use in different Artificial Intelligence (AI) scenarios are provided in this chapter. This chapter offers a familiarized assessment of the novel NIO while identifying and examining the main difficulties experienced in creating NIOA.