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Swarm Particle Optimization | Open Access Journals
Journal of Applied & Computational Mathematics

Journal of Applied & Computational Mathematics

ISSN: 2168-9679

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Swarm Particle Optimization

In computational science, particle swarm optimization is a computational method that optimizes a problem by attempting iteratively to improve a candidate solution with respect to a given quality measure. It solves a problem by having a population of candidate solutions, and by moving these particles in the search space on the basis of simple mathematical formulas. The movement of each particle is influenced by its locally best known position, but is also guided towards the best known search-space positions, which are updated as other particles find better positions. This will push the swarm towards the best solutions. PSO is a metaheuristic, as it makes few or no assumptions about optimizing the problem and can search for very large spaces of candidate solutions. Metaheuristics like PSO, however, do not guarantee that an optimal solution is ever found. PSO also does not use the gradient of the problem being optimized, which means that PSO does not require that the problem of optimization be differentiated as required by traditional methods of optimization such as gradient descent and quasi-newton methods.

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Citations: 1282

Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report

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