Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Swarm Intelligence (SI) is a field inspired by the collective behavior of social organisms, such as ants, bees, birds, and fish. This paper presents a comprehensive review of swarm intelligence principles, popular optimization algorithms, and their applications in engineering and computer science. We analyze classical swarm-based algorithms like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and more recent hybrid and adaptive approaches. The strengths, limitations, and performance metrics of these algorithms are discussed in comparison with conventional optimization techniques. Furthermore, we explore emerging trends, including multi-objective optimization, dynamic optimization, and real-world applications in robotics, scheduling, and machine learning. The paper aims to provide researchers and practitioners with a structured understanding of SI-based optimization methods, highlighting challenges and potential future directions.

Keywords: Swarm Intelligence, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, Metaheuristic, Optimization Algorithms, Multi-objective Optimization