ABC-PLOSS: a software tool for path-loss minimisation in GSM telecom networks using artificial bee colony algorithm

Abstract

In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and twofold path-loss improvement over the GA and the PSO techniques respectively.

Power loss minimization load flow studies using Artificial Bee Colony swarm intelligence technique

“…In contrast to existing results, in this paper, we propose the Artificial Bee Colony LFA (ABC-LFA) which uses swarm heuristics for constrained loss optimization of the load flow in a power system network in terms of the power mismatch. ABC is an emerging swarm intelligence technique inspired by the beautiful organizational and foraging ability of honeybee swarms while combining the global optimum capabilities of evolutionary computers with a fitness based model [11]. It was developed in [12] and has been widely applied by power system researchers in industries and academia.…”

An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique

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