BEST-FIT STRATEGY FOR OPTIMAL LOCATION OF TELECOMMUNICATION MAST USING HYBRID GENETIC-CUCKOO SEARCH ALGORITHMS

Abstract

The throughput and signal strength of a telecommunication system depends largely on effective installation of communication infrastructure especially the telecommunication mast so as to boost and support wider population. Estimating the population of installed masts from the assumed signal strength would influence management decisions with respect to some key indicators (Mast height, topology of the location) which in most cases are presented in imprecise and vague values. In this study, a hybrid metaheuristics mast location algorithm that explores possible search space to identify optimal location of the mast, estimate the signal strength and possibly infer the population was developed and used. Genetic algorithm (GA) and Cuckoo Search (CS) were the choice meta-heuristic optimization algorithms hybridized to explore and exploit the good qualities of both models in the search for optimal best-fit location. To cater for the system design phase, mathematical equations were formulated and used together with the object-oriented design methodology. Python and Hypertext Preprocessor (PHP) technologies were used for implementing the proposed system. The data of the coordinates were used in simulating the GA-CS model. The Root Mean Square Error (RMSE) was computed to validate model performance. Comparison of the developed model was carried out against GA and CS. The result shows that, the proposed GA-CS performed better