A New Optimization Model for Solving Center-Based Clustering Problem

In this paper, we propose a new optimization model for searching the optimal center of a one-dimensional set. Unlike the existing one, the developed optimization model does not involve any parameters and exponential function. The proposed model aims to decrease the overflow effect experienced by the existing exponential-based optimization model in a computational phase. The numerical simulation results in finding the optimal center of the randomly obtained data set reveal that our model is dependable. To test the superiority of the proposed model, a comparison is conducted with the existing one. The comparison results show that our model is more efficient based on three indicators, namely the smaller number of iterations, function evaluations, and faster running time.