Research Article Open Access

Natural Reforestation Optimization (NRO): A Novel Optimization Algorithm Inspired by the Reforestation Process

Fernando L. Rodríguez-Gallegos1, César A. Rodríguez-Gallegos2, Andrés A. Rodríguez-Gallegos3 and Carlos D. Rodríguez-Gallegos4
  • 1 Paderborn University, Germany
  • 2 Concordia University, Canada
  • 3 Universidad de Especialidades Espíritu Santo, Ecuador
  • 4 National University of Singapore (NUS), Singapore
Journal of Computer Science
Volume 16 No. 8, 2020, 1172-1184

DOI: https://doi.org/10.3844/jcssp.2020.1172.1184

Submitted On: 19 May 2020
Published On: 2 September 2020

How to Cite: Rodríguez-Gallegos, F. L., Rodríguez-Gallegos, C. A., Rodríguez-Gallegos, A. A. & Rodríguez-Gallegos, C. D. (2020). Natural Reforestation Optimization (NRO): A Novel Optimization Algorithm Inspired by the Reforestation Process. Journal of Computer Science, 16(8), 1172-1184. https://doi.org/10.3844/jcssp.2020.1172.1184

Abstract

This paper proposes a new meta-heuristic-based optimization algorithm for single-objective problems. The algorithm is called Natural Reforestation Optimization (NRO) and is inspired by the process in which natural reforestation takes place. The features of this algorithm (such as the distribution of the initial population, the exploration and exploitation mechanisms, the interactions between the particles, the stopping criteria, among others) are discussed and analyzed to show how they are applied to enhance the search of the global solution. The performance of this algorithm is tested with standard single-objective optimization problems (which contain from 2 to 20 optimization variables) and is compared with other optimization algorithms. The results reveal that in general, the NRO algorithm produces solutions close to the global optimal and is able to surpass the other optimization algorithms for many of the benchmark functions. The current study shows the qualities of the NRO algorithm and serves as the starting point for further investigation to take place to keep improving its capabilities.

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Keywords

  • Meta-Heuristic Optimization Algorithm
  • Single-Objective Optimization
  • Global Optimization