Improved Differential Evolution Algorithm for Location Management in Mobile Computing

Differential evolution has emerged as one of the fast, robust, and efficient global optimization approach of current interest. In this paper, an application of differential evolution to the location management problem in mobile computing is described. To improve the convergence rate and the quality of solutions of the differential evolution algorithm, an effective population initialization strategy was used and a double neighborhood structure local search technique was embedded in the differential evolution algorithm. The improved algorithm was applied to test-networks with different scale in experiments. Superiority of the proposed algorithm is demonstrated by comparing it with the state-of-the-art algorithms and classical differential evolution algorithm.