Automatic Business Location Selection through Particle Swarm Optimization and Neural Network

Abstract

Location selection is an important part of running a business. A good business location can greatly increase customer flow, reduce operating costs, and increase business revenue. Therefore, it is necessary to select an appropriate location for business premises. In this research, we propose a method combining machine learning and particle swarm optimization for the business location selection. In the proposed method, we first apply the particle swarm algorithm to optimize the proportional coefficient of the influence of different surrounding commercial places on the site selection of the research site. Then we use a machine learning method to perform the binary classification on the sites to judge whether a geographic location is a good site selection point. The proposed method is compared with several common location selection methods such as random selection method and k-nearest neighbor method. The experimental results have validated the effectiveness of our proposed method for business location selection.