A Comparative Study on Meta-Heuristic Algorithms For Solving the RNP Problem


The continuous increases in the size of current telecommunication infrastructureshave led to the many challenges that existing algorithms face in underlyingoptimization. The unrealistic assumptions and low efficiency of the traditionalalgorithms make them unable to solve large real-life problems at reasonable times.The use of approximate optimization techniques, such as adaptive metaheuristicalgorithms, has become more prevalent in a diverse research area. In this paper, weproposed the use of a self-adaptive differential evolution (jDE) algorithm to solvethe radio network planning (RNP) problem in the context of the upcominggeneration 5G. The experimental results prove the jDE with best vector mutationsurpassed the other metaheuristic variants, such as DE/rand/1 and classical GA, interm of deployment cost, coverage rate and quality of service (QoS).