A Brief Review of Nature-Inspired Algorithms for Optimization

TL;DR

This paper reviews various nature-inspired algorithms, including swarm intelligence and bio-inspired methods, highlighting their diversity, efficiency, and potential for solving real-world optimization problems.

Contribution

It provides a comprehensive overview of existing nature-inspired algorithms to inspire future research and development in the field.

Findings

Some algorithms are highly efficient for real-world problems

Many algorithms remain insufficiently studied

The review encourages further exploration of diverse algorithms

Abstract

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very efficient and thus have become popular tools for solving real-world problems. Some algorithms are insufficiently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.