Cumulative Major Advances in Particle Swarm Optimization from 2018 to the Present: Variants, Analysis and Applications

Abstract

Particle Swarm Optimization (PSO) is a key tool in Artificial Intelligence, is well-known to the public for its effectiveness in addressing complex and diverse problems. It possesses strong global search capabilities and robustness, serving as a powerful tool for problem-solving. PSO can handle multiple solutions simultaneously, accelerate problem-solving processes through parallel computing, and dynamically adjust search strategies based on the complexity and variability of problems, thereby adapting to different types of problems. As an efficient swarm intelligence-based algorithm, PSO has been a highly regarded Swarm Intelligence (SI) model since its establishment in 1995, undergoing numerous modifications and innovations to address various complex real-world problems. This article extensively investigates the variants and applications of PSO. Conducted based on a Systematic Review (SR) process, it delves deep into the research papers published in recent years, encompassing different algorithms, a wide range of application domains, potential issues, and future prospects. Specifically, this article reviews existing research methods and their applications, focusing on single-objective algorithms published from 2018 to the present, including but not limited to multiple swarms or multiple samples, learning mechanisms, hybrid algorithms, and their applications in various interdisciplinary fields such as mechanical engineering, civil engineering, power system, energy, and Internet of Things (IoT). Each paper contains practical guidance and inherent limitations, prompting discussions on their applications and outlining potential challenges of PSO, as well as guiding future research directions.