Some polynomial programming problems solving with a clustering algorithm

In my study I describe an optimum search clustering algorithm and I demonstrate its capabilities by the approximate solving of a few polynomial programming problems. This clustering algorithm determines in a given number the local or global minima of the multimodal functions as the center of the clusters. Unlike normal clustering algorithms it approaches the extrema continuously in a single phase, on the basis of the sample points. The method determines the values and places of the extrema of the usual test examples with an accuracy of about 0.01. The OSCA can be used to solve linear or nonlinear constrained problems, and as the examples demonstrate it gives a good approximation of the global optimum in case of benchmark optimization problems as well.