ORCID Profile
0000-0002-9513-0355
Current Organisation
Shahid Rajaee Teacher Training University
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Publisher: IEEE
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 28-12-2022
Publisher: Wiley
Date: 31-03-2017
DOI: 10.1002/DAC.3315
Publisher: Wiley
Date: 25-05-2017
DOI: 10.1002/ETT.3199
Publisher: IEEE
Date: 12-2015
Publisher: Oxford University Press (OUP)
Date: 13-06-2023
DOI: 10.1093/JCDE/QWAD053
Abstract: The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the ersity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population ersity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and ersity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at github.com/MohammedQaraad/GMSMFO-algorithm.
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
No related grants have been discovered for Mohammed Qaraad.