A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

Hegazy Rezk, A. G. Olabi, Tabbi Wilberforce, Enas Taha Sayed

Research output: Contribution to journalReview articlepeer-review


For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed.
Original languageEnglish
Article number5732
Number of pages24
Issue number7
Early online date24 Mar 2023
Publication statusPublished - Apr 2023

Bibliographical note

Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Funding Information:
This study was sponsored by the Prince Sattam bin Abdulaziz University through project number 2023/RV/013.


  • battery storage
  • fuel cells
  • metaheuristic optimization
  • parameters identification
  • photovoltaic


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