A Note on the Griewank Test Function

Authors:
M. Locatelli
Affiliations:
Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, 10149 Torino, Italy (e-mail: locatelli@di.unito.it)
Venue:
Journal of Global Optimization
Year:
2003

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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.