Swarm Intelligence in Modern Engineering a Comprehensive Review of Applications, Performance and Emerging Trends

Abstract

Modern engineering systems increasingly encounter complex, high-dimensional optimization problems that challenge traditional solution methods. Swarm intelligence (SI) algorithms, inspired by the collective behavior of biological systems, offer robust and adaptable alternatives. This review systematically explores the development and application of key SI techniques-Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO)-within engineering domains from 2020 to 2025. Drawing on recent literature, the paper identifies major application areas in mechanical, structural, power, energy, civil, and infrastructure engineering. It evaluates algorithmic performance trends, emphasizing the superior convergence and robustness of hybrid approaches, along with their growing integration with machine learning. The review also highlights advances in multi-objective optimization and the expanding use of SI in emerging fields such as IoT and cybersecurity. The findings underscore the increasing significance of SI in next-generation engineering systems, particularly in autonomous technologies and smart infrastructure, while outlining key directions for future research and practical deployment.