Particle swarm optimization: Difference between revisions
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Particle Swarm Optimization (PSO) is an optimization method in which multiple candidate solutions migrate through the solution space. PSO does not require that the objective function be differentiable. The method should be applied to one or more of the 'well-known' optimization test cases e. g. the Rosenbrock ('banana') function. |
Particle Swarm Optimization (PSO) is an optimization method in which multiple candidate solutions migrate through the solution space. PSO does not require that the objective function be differentiable. The method should be applied to one or more of the 'well-known' optimization test cases e. g. the Rosenbrock ('banana') function. |
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Revision as of 06:25, 31 July 2015
Particle swarm optimization is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.
Particle Swarm Optimization (PSO) is an optimization method in which multiple candidate solutions migrate through the solution space. PSO does not require that the objective function be differentiable. The method should be applied to one or more of the 'well-known' optimization test cases e. g. the Rosenbrock ('banana') function.