Pseudorandomness in Central Force Optimization

  title={Pseudorandomness in Central Force Optimization},
  author={Richard A. Formato},
  • R. Formato
  • Published 2 January 2010
  • Computer Science
  • ArXiv
Central Force Optimization is a deterministic metaheuristic for an evolutionary algorithm that searches a decision space by flying probes whose trajectories are computed using a gravitational metaphor. CFO benefits substantially from the inclusion of a pseudorandom component (a numerical sequence that is precisely known by specification or calculation but otherwise arbitrary). The essential requirement is that the sequence is uncorrelated with the decision space topology, so that its effect is… 

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a, k AS EXT LOCAL i%, m% a = 10## : k = 100## : m% = 4 '[NOTE IN CORRECTION 02-03-2010: Coefficient “a” was incorrectly set to 5 in the original version
  • LOCAL Offset,
  • 2010