Next Generation Wireless Networks (NGWN) are predicted to be heterogeneous in nature. This is achieved through the integration of different Radio Access Technologies (RATs) over a common platform. Common Radio Resource Management (CRRM) was proposed to manage radio resource utilization in heterogeneous wireless networks and to provide required Quality of Service (QoS) for allocated calls. RAT selection algorithms are an integral part of the CRRM algorithms. Their role is to decide, when a new or Vertical Handover (VHO) call is requested, which of the available RATs is most suitable to fit the need of the incoming call and when to admit them. In this paper, we propose an intelligent hybrid RAT selection approach for mobility optimization (patent pending 1) which includes sorting available RATs, collecting information on each RAT using the IEEE P1900.4 Protocol, and making decisions for selecting the most suitable RAT for incoming calls. A comparison for the performance of centralized load-balancing, distributed and the proposed mobility optimization algorithms is presented in terms of new calls blocking probability, VHO calls dropping probability and satisfactions probability. Simulation results show that the proposed algorithm performs better than the centralized load-balancing and distributed algorithms in terms of blocking, dropping and satisfactions probabilities.