<![CDATA[As cellular networks evolve into microcells or smaller ones, the number of sites required to serve an area will also increase, making network optimization impossible to perform manually. This study provides an overview of the implementation of Genetic Algorithms in solving radius and azimuth configuration problems for cell coverage. The main contribution of this study is the use of Fixed Step and Variable Step azimuth selection methods in the Genetic Algorithm coding process. This study also raises the issue of interference detection mechanisms in two adjacent cells. The problem presentation method uses a model with a measurable level of complexity by deriving from a homogeneously distributed layout model. This study uses Matlab in building an algorithm simulator and MapInfo in displaying the results visually. The results show that the proposed Variable Step method provides better optimization performance than the Fixed Step method, which was only able to resolve 62.5% of interference spots. To account for the stochastic nature of the Genetic Algorithm, the simulation was executed 10 independent times using identical parameter settings. Statistical evaluation indicates that the algorithm achieved an average fitness value of 59.40 with a standard deviation of 3.84, while requiring an average computational time of 635.41 seconds with a standard deviation of 9.23 seconds. Furthermore, the algorithm reached convergence at an average generation of 957.10 with a standard deviation of 34.98 generations. These results demonstrate that the proposed algorithm exhibits good stability across multiple runs, although it was unable to reach the global optimum in all simulations.]]>
Copyrights © 2026
