Swarm Intelligence: Applications and Implementations in Autonomous Systems

Abstract

Swarm intelligence (SI) is a collective behavior exhibited by groups of simple agents, such as ants, bees, and birds, which can achieve complex tasks that would be difficult or impossible for a single individual (Dorigo et al., 2019). The collective behavior of these organisms is characterized by decentralized decision-making, self-organization, adaptive responses to environmental changes, and emergent properties that are not present in individual organisms ( Beni & Wang, 2004 ). SI algorithms emulate these features to solve complex optimization, control, classification, clustering, routing, and prediction problems in diverse domains, such as engineering, robotics, biology, economics, social sciences, and humanities. There are two main categories of SI algorithms: swarm-based algorithms and swarm-inspired algorithms ( Dorigo & Gambardella, 1996 ). Swarm-based algorithms involve the simulation of a population of individuals (agents) that interact with each other and their environment to achieve a collective goal. Examples of swarm-based algorithms include ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and firefly algorithm (FA) ( Kennedy & Eberhart, 1995 ; Karaboga & Basturk, 2007 ; Yang, 2010). Swarm-inspired algorithms, on the other hand, extract specific mechanisms or principles from natural swarms and incorporate them into conventional optimization or machine learning algorithms. Examples of swarm-inspired algorithms include artificial immune systems (AIS), bacterial foraging optimization (BFO), and grey wolf optimizer (GWO) ( Dasgupta & González, 2002 ; Passino, 2002 ; Mirjalili et al., 2014 ).