Abstract
In this paper, we develop a new theoretical framework by means of the absorbin Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.
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Project supported by the Science Foundation of Shanghai Municipal Commission of Education
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Shi, Dh., Peng, Jp. A new theoretical framework for analyzing stochastic global optimization algorithms. J. of Shanghai Univ. 3, 175–180 (1999). https://doi.org/10.1007/s11741-999-0054-z
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DOI: https://doi.org/10.1007/s11741-999-0054-z