Abstract
The unconstrained global programming problem is addressed using an efficient multi-start algorithm, in which parallel local searches contribute towards a Bayesian global stopping criterion.
The stopping criterion, denoted the unified Bayesian global stopping criterion, is based on the mild assumption that the probability of convergence to the global optimum x* is comparable to the probability of convergence to any local minimum \( \tilde x_j \) .
The combination of the simple multi-start local search strategy and the unified Bayesian global stopping criterion outperforms a number of leading global optimization algorithms, for both serial and parallel implementations. Results for parallel clusters of up to 128 machines are presented.
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Bolton, H.P.J., Schutte, J.F., Groenwold, A.A. (2000). Multiple Parallel Local Searches in Global Optimization. In: Dongarra, J., Kacsuk, P., Podhorszki, N. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2000. Lecture Notes in Computer Science, vol 1908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45255-9_15
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DOI: https://doi.org/10.1007/3-540-45255-9_15
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