Particle swarm optimization: Difference between revisions

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The method should demonstrated by application to the 2D Rosenbrock function, and possibly other standard or well-known optimization test cases.
The method should be demonstrated by application of the 2D Rosenbrock function, and possibly other standard or well-known optimization test cases.
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Revision as of 05:18, 1 August 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 under the influence of 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 demonstrated by application of the 2D Rosenbrock function, and possibly other standard or well-known optimization test cases.

References:

  • [Particle Swarm Optimization[1]]
  • [Rosenbrock function[2]]
  • [Test functions for optimization[3]]