Partitional Clustering using a Generalized Visual Stochastic Optimizer

Visual optimization is a very interesting topic to the application users for many purposes. It enables the user with an interactive platform where, by varying different parameter settings, one can customize a solution. Several attempts of developing generalized evolutionary optimizers are found in literature which work well for function optimization problems only. Solving combinatorial optimization problems on such a general platform is a difficult task. In this paper, we have tried to solve partitional clustering problem using a generalized visual stochastic optimization algorithm that was initially developed for function optimization problems only.