Author(s)

John Guenther, Herbert K. H. Lee

Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA, USA.

The black box functions found in computer experiments often result in multimodal optimization programs. Optimization that focuses on a single best optimum may not achieve the goal of getting the best answer for the purposes of the experiment. This paper builds upon an algorithm introduced in [1] that is successful for finding multiple optima within the input space of the objective function. Here we introduce an alternative cluster search algorithm for finding these optima, making use of clustering. The cluster search algorithm has several advantages over the earlier algorithm. It gives a forward view of the optima that are present in the input space so the user has a preview of what to expect as the optimization process continues. It employs pattern search, in many instances, closer to the minimum’s location in input space, saving on simulator point computations. At termination, this algorithm does not need additional verification that a minimum is a duplicate of a previously found minimum, which also saves on simulator point computations. Finally, it finds minima that can be “hidden” by close larger minima.

Bayesian Statistics, Treed Gaussian Process, Emulator, DBSCAN, Optimization

Guenther, J. and Lee, H. (2016) Cluster Search Algorithm for Finding Multiple Optima. Applied Mathematics, 7, 736-752. doi: 10.4236/am.2016.77067.