Particle Swarm Optimization Application for Multiple Attribute Decision Making in Vertical Handover in Heterogenous Wireless Networks
In wireless heterogenous networks, mobile terminals are covered by different wireless networks with varying quality of services to ensure the delivery of different classes of services. In this paper, Particle Swarm Optimization (PSO) was applied to the distance to ideal alternative (DIA) technique in the framework of network selection in a heterogenous wireless network. The PSO was applied to overcome the subjectivity and bias in the weights’ assignment process used in multiple attribute decision making (MADM). The PSO was utilized to optimize the weights of the DIA method through the maximization of the absolute value of the summation of the ranking differences among candidate networks. In this regard, two different optimization functions were introduced and used to generate the optimum weights. The performance of the PSO-based handover for the DIA method was investigated in terms of ranking difference, ranking abnormalities and network selection. The results show that the proposed PSO-based weights’ assignment technique increased the ranking difference and reduced the ranking abnormalities without degrading the network selection when compared to the conventional DIA technique. The results of this paper are expected to widen the application of the DIA method and other MADM techniques to the handover process in wireless networks and other decision-based challenges in other fields.