Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Abstract

This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.