Abstract
Cybernetic optimization by simulated annealing (COSA) is a method of parallel processing for solving discrete optimization problems. This paper extends the theory of COSA to the continuous domain. This is done by applying the concept of probabilistic feedback control to the generation of candidate solutions in continuous variable problems. Three general principles of candidate generation are presented leading to the formulation of the candidate generation criterion and its theoretical implications. A practical method of generating candidate solutions is also presented in which the generation of candidate solutions is achieved by making the magnitude of the expected step size to candidate solutions functionally dependent on the proximity of the parallel processors in the solution space. Experimental results show that this method of generating candidate solutions accelerates the convergence of the parallel processors to the global optimum.
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© 1999 Springer Science+Business Media New York
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Fleischer, M.A. (1999). Generalized Cybernetic Optimization: Solving Continuous Variable Problems. In: Voß, S., Martello, S., Osman, I.H., Roucairol, C. (eds) Meta-Heuristics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5775-3_28
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DOI: https://doi.org/10.1007/978-1-4615-5775-3_28
Publisher Name: Springer, Boston, MA
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