In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a population of candidate solutions with regard to a given measure of quality. It solves a problem through interactions among a population of candidate solutions, dubbed particles, moving the particles around in the search-space according to simple mathematical formulae that adjust each particle's position and velocity. Each particle's movement is influenced by its own best known position so far, and by the best known position in its topological neighborhood ; vectors are updated as better positions are found. This is expected to move the swarm toward good solutions.