The intelligent collective behavior of certain animals, such schools of fish or flocks of birds, served as inspiration for the population-based stochastic optimization technique called particle swarm optimization (PSO). Many updates and enhancements have been made to it since its 1995 debut. As the technique has been studied further, researchers have published theoretical investigations into parameter effects, developed new applications in diverse domains, generated new versions tailored to varied demands, and given further algorithmic variants. In addition to providing an overview of the PSO's background and development, this page offers a conceptual analysis of the acronym. Next, we will have a look at the current research and practical applications surrounding discrete and parallel PSO algorithms, topological structure, parameter selection, multi-objective optimization, and engineering applications of PSO. The paper concludes with a review of the current state of affairs and some suggestions for further study.

The intelligent collective behavior of certain animals, such schools of fish or flocks of birds, served as inspiration for the population-based stochastic optimization technique called particle swarm optimization (PSO).