In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by way of iteratively trying to enhance a candidate solution with reference to a given measure of quality. It solves a problem through having a populace of candidate solutions, here dubbed particles, and shifting these particles around inside the search-space according to simple mathematical formulae over the particle's role and velocity. Each particle's motion is influenced by way of its local excellent recognised function, however is also guided closer to the high-quality acknowledged positions within the seek-space, which can be up to date as better positions are located through other particles. This is predicted to transport the swarm in the direction of the fine solutions.

PSO is at the beginning attributed to Kennedy, Eberhart and Shi and turned into first supposed for simulating social behaviour, as a stylized illustration of the motion of organisms in a chicken flock or fish school. The algorithm turned into simplified and it turned into determined to be appearing optimization. The e-book by Kennedy and Eberhart describes many philosophical elements of PSO and swarm intelligence. An good sized survey of PSO applications is made with the aid of Poli. Recently, a complete assessment on theoretical and experimental works on PSO has been posted with the aid of Bonyadi and Michalewicz.

PSO is a metaheuristic because it makes few or no assumptions about the problem being optimized and may seek very large spaces of candidate solutions. However, metaheuristics which include PSO do not assure an finest solution is ever located. Also, PSO does now not use the gradient of the hassle being optimized, because of this PSO does now not require that the optimization trouble be differentiable as is required by way of traditional optimization methods including gradient descent and quasi-newton techniques.

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