The article provides an overview of Particle Swarm Optimization (PSO), a computational intelligence technique inspired by social interaction. Since its introduction in 1995, PSO has evolved with new versions, applications, and theoretical studies. The authors, Riccardo Poli, James Kennedy, and Tim Blackwell, offer a snapshot of the field from their perspective, covering algorithm variations, current and ongoing research, applications, and open problems. The article is organized into several sections, including an introduction, population dynamics, social network influences, variants of PSO, theoretical analyses, successful applications, open problems, and conclusions. The initial ideas of Kennedy and Eberhart aimed to produce computational intelligence by simulating social interactions, leading to the development of PSO as a powerful optimization method. Each particle in the swarm evaluates the objective function at its current position and moves based on its own history and the swarm's best positions, with random perturbations. The swarm's collective movement aims to find the optimum of the fitness function. The article emphasizes that the power of PSO lies in the interactions among particles rather than individual particles alone.The article provides an overview of Particle Swarm Optimization (PSO), a computational intelligence technique inspired by social interaction. Since its introduction in 1995, PSO has evolved with new versions, applications, and theoretical studies. The authors, Riccardo Poli, James Kennedy, and Tim Blackwell, offer a snapshot of the field from their perspective, covering algorithm variations, current and ongoing research, applications, and open problems. The article is organized into several sections, including an introduction, population dynamics, social network influences, variants of PSO, theoretical analyses, successful applications, open problems, and conclusions. The initial ideas of Kennedy and Eberhart aimed to produce computational intelligence by simulating social interactions, leading to the development of PSO as a powerful optimization method. Each particle in the swarm evaluates the objective function at its current position and moves based on its own history and the swarm's best positions, with random perturbations. The swarm's collective movement aims to find the optimum of the fitness function. The article emphasizes that the power of PSO lies in the interactions among particles rather than individual particles alone.