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Multi-Objective Optimization Using Evolutionary Algorithms
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A fast and elitist multiobjective genetic algorithm: NSGA-II
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Efficient channel allocation to mobile hosts aims to minimize the number of blocked hosts and is of utmost importance in a mobile computing network. Also, to achieve highly reliable data transmission, wireless mobile networks require efficient and reliable link connectivity regardless of terminal mobility, and thus reliable traffic performance. A mobile network consists of mobile nodes, base stations, links, etc. that are often prone to failure. The multi-objective optimization problem (MOP), does not offer one best solution with respect to all the objectives. The aim is to determine the trade-off surface, which is a set of nondominated solution points known as Pareto-optimal. The two objectives addressed in this paper are to minimize the number of blocked hosts while maximizing the reliability of the data transmission. A multi-objective optimization is carried out to optimize both objectives simultaneously. The elitist NSGA-II (non-dominated sorting genetic algorithm) has been used as an evolutionary optimization technique to solve this problem.Apopulation of efficient solutions results when the termination condition is satisfied. Also the Pareto-optimal fronts obtained provide a wide range of trade-off operating conditions from which an appropriate operating point may be selected by the decision maker. The experimental results are presented and analyzed for overall evaluation of the performance of the proposed work. Further, comparison of the results with the two recent earlier models reveals that the proposed work performs better in serving mobile hosts as it caters to two objectives simultaneously.