In the Weber location problem which was proposed for optimal location of industrial enterprises, the aim is to find the location of a point such that the sum of weighted distance between this point and a finite number of existing points is minimized. This popular model is widely used for optimal location of equipment and in many sophisticated models of cluster analysis such as detecting homogeneous production batches made from a single production batch of raw materials. The well-known iterative Weiszfeld does not converge efficiently to the optimal solution when the solution either coincides or nearly coincides with one of the demands point which is not the optimum. We propose a modified Particle Swarm Optimization (PSO) algorithm. The velocity update of the PSO is modified to enlarge the search space and enhance the global search ability. The preliminary results of these algorithms are analyzed and compared.