Soft computing technique for cost reduction in cellular network

In cellular network location management is a fundamental and complex problem which deals how to track the subscriber on move. Some amount of cost is incurred for the subscriber during the movement in a particular service area. This cost basically involved location update cost and paging cost. The main objective of this work is to reduce this total cost which includes this location update cost and paging cost by using different evolutionary techniques. This paper presents binary genetic algorithm to solve the location management problem by partitioning the given cellular network into location areas so as to minimize the location management cost. Binary Genetic Algorithm (BGA) is a meta-heuristic method which has presented to be a very powerful widely used, prominent and a population-based optimization approach. Among the entire evolutionary techniques Genetic algorithm is a biological, simple inspired optimization with reduced complexity. With the help of this algorithm optimal location areas are obtained corresponding to the minimized cost. Simulation results and optimal location area planning for different networks are demonstrated and discussed. The effectiveness of GA result is shown to be effective using less number of iteration.