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Review Article |

Garra Rufa Technique for Unit Commitment Optimization: A Comparative Analysis with Metaheuristic Algorithms

Author(s): Suhaib Al-Karawi1*,Aws Al-Taie2,Aqeel S. Jaber3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 2

Publisher : FOREX Publication

Published : 30 June 2026

e-ISSN : 2347-470X

Page(s) : 653-660

DOI: https://doi.org/10.37391/IJEER.140240

Abstract

Optimization techniques have received much attention in the recent years in the area of power system research. The success of any optimization process relies much on the proper choice of the algorithm and its parameters, depending on the actual problem considered. Among different approaches of optimization, Garra Rufa Optimization (GRO) has shown a great potential in the ‎operation of power systems. This study proposes GRO for the unit commitment (UC) problem. Typically, the solution of a UC problem consists of two major stages, finding the generating units to be committed and the level of generation to be allocated ‎among these units to minimize total operating cost while meeting load demand and system constraints. Accordingly, priority list method is applied for the first stage whereas GRO is used to optimize the second stage. To check the performance of the proposed method, a 10 generating units test system was used. The cost function values were taken as the performance indicators to compare GRO with Gray Wolf (GWO), Spider Wasp (SWO), Particle Swarm (PSO) optimization algorithms. The time for each method was calculated for 50 runs. Results for GWO, SWO, PSO, and GRO were 0.0811, 0.118, 0.0783, and 0.0793 second, respectively. Although all methods can be considered fast, GRO was the second fastest, with a slight time difference from PSO. Despite that, it is considered practically acceptable due to the noticeable improvement in the accuracy of the results. The results proves that the proposed GRO has better performance in terms of economical generator selection and least error between total generation and system demand.

Keywords: Garra Rufa, Gray Wolf, Particle Swarm Optimization, Spider Wasp, Unit Commitment.




Suhaib Al-Karawi,Department of Electrical Engineering, University of Technology-Iraq, Baghdad, Iraq; Email: krammohanee@gmail.com

Aws Al-Taie, Department of Electrical Engineering, University of Technology-Iraq, Baghdad, Iraq; Email: aws.h.mohammed@uotechnology.edu.iq

Aqeel S. Jaber, Independent Researcher, Helsinki, Finland;Email: aqe77el@yahoo.com

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Suhaib Al-Karawi, Aws Al-Taie, and Aqeel S. Jaber (2026), Garra Rufa Technique for Unit Commitment Optimization: A Comparative Analysis with Metaheuristic Algorithms. IJEER 14(2), 653-660. DOI: 10.37391/IJEER.140240.