**1. Overview**

Swarm intelligence (SI) is a collective behaviour 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. The collective behaviour 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. 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 [1].

SI algorithms can be classified into two main categories: swarm-based algorithms and swarm-inspired algorithms [2]. 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) [3], particle swarm optimization (PSO) [4], artificial bee colony (ABC) [5], and firefly algorithm (FA) [6]. 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) [7], bacterial foraging optimization (BFO) [8], and grey wolf optimizer (GWO) [9].

The success of SI algorithms is attributed to their ability to efficiently explore a large search space, converge to optimal or near-optimal solutions, and handle multiple objectives or constraints simultaneously. The collective intelligence of the swarm enables the sharing and exchange of information, the exploitation of promising regions, and the avoidance of suboptimal regions. Furthermore, the decentralized and distributed nature of the swarm allows for scalability, robustness, fault-tolerance, and adaptivity to dynamic or uncertain environments [10].

Despite their advantages, SI algorithms face several challenges and limitations, such as premature convergence, scalability issues, sensitivity to parameter settings, lack of theoretical guarantees, and difficulty in interpreting or explaining the obtained results. Researchers have proposed various approaches to overcome these challenges, such as hybridization with other optimization or machine learning techniques, dynamic adaptation of parameters, incorporation of domain knowledge, and rigorous analysis of convergence properties.
