Improving Genetic Algorithm to Solve Multi-objectives Optimal of Upgrading Infrastructure in NGWN

A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, I propose a new genetic algorithm based on a combination of two populations to solve multi-objective optimization infrastructure upgrade problem in NGWN. Network topology model has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. My objective function is the costs of connection from sources to concentrators such as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. I generate two populations satisfies constraints and combine its to build solutions and evaluate the performance of my algorithm with data randomly generated. The experimental results show that this approach is appropriate and effective Finally, I have applied this algorithm to planning of upgrade infrastructure in telecommunication networks in Haiphong city