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 |
Particle Swarm Optimization (PSO) is an optimization method in which multiple candidate solutions ('particles') migrate through the solution space, influenced by local and global best known positions. PSO does not require that the objective function be differentiable and can optimize over very large problem spaces, but is not guaranteed to converge. |
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The method should be applied to the 2D Rosenbrock 'banana' function, and possibly other standard or well-known optimization test cases. |
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Revision as of 14:42, 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 ('particles') migrate through the solution space, influenced by local and global best known positions. PSO does not require that the objective function be differentiable and can optimize over very large problem spaces, but is not guaranteed to converge.
The method should be applied to the 2D Rosenbrock 'banana' function, and possibly other standard or well-known optimization test cases.