PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. Recently, a comprehensive review on theoretical and experimental works on PSO has been published by Bonyadi and Michalewicz. An extensive survey of PSO applications is made by Poli. The book by Kennedy and Eberhart describes many philosophical aspects of PSO and swarm intelligence. The algorithm was simplified and it was observed to be performing optimization. PSO is originally attributed to Kennedy, Eberhart and Shi and was first intended for simulating social behaviour, as a stylized representation of the movement of organisms in a bird flock or fish school.
This is expected to move the swarm toward the best solutions. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles.
It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. In computational science, particle swarm optimization ( PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
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