2002/01/01 by Maurice Clerc, James Kennedy · 10 citations
Computer Science · #Metaheuristic Optimization Algorithms Research #Evolutionary Algorithms and Applications #Advanced Multi-Objective Optimization Algorithms
paper · doi:10.1109/4235.985692
openalex publication_date 2002/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.