glcCluster is a global optimization algorithm, developed by Prof.Dr. Kenneth Holmström. It is a hybrid algorithm, combining the DIRECT global optimization algorithm, a clustering algorithm and local search. glcCluster is using one of the following DIRECT algorithms: glcDirect (default), glcFast or glcSolve, for global search (Step 1). Step 2 is an adaptive clustering algorithm to find a suitable number of clusters, where the best point in each cluster is then used as an initial point for a local search (Step 3). The 4th step is to run the DIRECT algoirithm once again, to possibly improve. If the DIRECT algorithm improves the best point, a local search is finally made as Step 5 with the new best point(s) as starting points. The routine glcCluster implements an extended version of DIRECT that handles problems with both nonlinear and integer constraints.

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  1. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)