Abstract
In this paper we analyze a widely employed test function for global optimization, the Griewank function. While this function has an exponentially increasing number of local minima as its dimension increases, it turns out that a simple Multistart algorithm is able to detect its global minimum more and more easily as the dimension increases. A justification of this counterintuitive behavior is given. Some modifications of the Griewank function are also proposed in order to make it challenging also for large dimensions.
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References
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Locatelli, M. A Note on the Griewank Test Function. Journal of Global Optimization 25, 169–174 (2003). https://doi.org/10.1023/A:1021956306041
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DOI: https://doi.org/10.1023/A:1021956306041