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

  • Abeer Sufyan Khalil Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Rawaa Dawoud Al-Dabbagh Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

Abstract

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

Published
Jul 19, 2019
How to Cite
KHALIL, Abeer Sufyan; AL-DABBAGH, Rawaa Dawoud. A Comparative Study on Meta-Heuristic Algorithms For Solving the RNP Problem. Iraqi Journal of Science, [S.l.], p. 1639-1648, july 2019. ISSN 2312-1637. Available at: <http://scbaghdad.edu.iq/eijs/index.php/eijs/article/view/917>. Date accessed: 22 aug. 2019.
Section
Computer Science