Abstract
A class of parallel characteristical algorithms for global optimization ofone-dimensional multiextremal functions is introduced. General convergence andefficiency conditions for the algorithms of the class introduced areestablished. A generalization for the multidimensional case is considered.Examples of parallel characteristical algorithms and numerical experiments arepresented.
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Grishagin, V.A., Sergeyev, Y.D. & Strongin, R.G. Parallel Characteristical Algorithms for Solving Problems of Global Optimization. Journal of Global Optimization 10, 185–206 (1997). https://doi.org/10.1023/A:1008242328176
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DOI: https://doi.org/10.1023/A:1008242328176