Mdp-based network selection scheme by genetic algorithm and simulated annealing for vertical-handover in heterogeneous wireless networks

Abstract

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA–SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.