This book provides a comprehensive overview of the foundational attributes of the Particle Swarm Optimization(PSO) algorithm, including general descriptions, topological structures, evaluation metrics, and diversity. It explores in depth the issues of premature convergence and the kinematic characteristics of the Gbest (Global best), Pbest (Personal best), and standard particle models. The book also introduces a stability criterion based on dynamic time-varying systems and examines the Markov properties and convergence behavior of the standard PSO algorithm. For single-objective optimization problems, the book presents four paradigmatic design philosophies and enhancement strategies for PSO algorithms. In addressing multi-objective optimization challenges, it offers a systematic analysis and design methodology for multi-objective PSO. This book is ideal for researchers in the fields of swarm intelligence and optimization techniques. It aids scholars and professionals in gaining a deep understanding of swarm intelligence methodologies, with a particular focus on the systematic characteristics, stability, convergence, and |