Classifying Nature-Inspired Swarm Algorithms for Sustainable Autonomous Mining

Abstract

Over the resent decade, swarm-based algorithms have been utilized for automation in the mining industry. However, there is lack of understanding of their specific contributions at different stages of the mining process, in the broader sense. This paper classifies the optimization of mining lifecycle and swarm robotic systems based on reviewing nine nature-inspired algorithms for sustainable mining. Namely, the following swarm-based algorithms have been considered: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Krill Herd Algorithm (KHA), Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA). In this study, we conduct a systematic review of their impact on spatial organization, navigation, and collective decision-making, which in their turn can help to improve exploration accuracy, mine planning precision, and transportation efficiency. This research highlights the utility of nature-inspired algorithms that can contribute to specific mining phases and operations and should allow to achieve a more efficient and targeted mine optimization, greater environmental sustainability and improved mine safety.