ORIGINAL RESEARCH article
Front. Appl. Math. Stat.
Sec. Optimization
Volume 11 - 2025 | doi: 10.3389/fams.2025.1640044
This article is part of the Research TopicOptimization for Low-rank Data Analysis: Theory, Algorithms and ApplicationsView all 6 articles
EESB-FDO: Enhancing the Fitness Dependent Optimizer Through a Modified Boundary Handling Mechanism
Provisionally accepted
- Charmo University, Chamchamal/Sulaimani, Iraq
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Abstract: The Fitness Dependent Optimizer (FDO) has recently obtained attention as an effective metaheuristic for solving different optimization problems. However, it faces limitations in exploitation and convergence speed. To overcome these challenges, this study introduces two enhanced variants: enhancing exploitation through stochastic boundary for FDO (EESB-FDO) and enhancing exploitation through boundary carving for FDO (EEBC-FDO). In addition, the ELFS strategy is proposed to constrain Levy flight steps, ensuring more stable exploration. Experimental results explain that these modifications significantly improve the performance of FDO compared to the original version. To evaluate the performance of the EESB-FDO and EEBC-FDO, three primary categories of benchmark test functions were utilized: classical, CEC 2019, and CEC 2022. The assessment was further supported by the application of statistical analysis methods to ensure a comprehensive and rigorous performance evaluation. The performance of the proposed EESB-FDO and EEBC-FDO algorithms was evaluated through comparative analysis with several existing FDO modifications, as well as with other well-established metaheuristic algorithms, including the Arithmetic Optimization Algorithm (AOA), the Learner Performance-Based Behavior Algorithm (LPB), the Whale Optimization Algorithm (WOA), and the Fox-inspired Optimization Algorithm (FOX). The statistical analysis indicated that both EESB-FDO and EEBC-FDO exhibit better performance compared to the aforementioned algorithms. Furthermore, a final evaluation involved applying EESB-FDO and EEBC-FDO to four real-world optimization problems: the Gear Train Design Problem, the Three-Bar Truss Problem, the Pathological IgG Fraction in the Nervous System, and the Integrated Cyber-Physical Attack on a Manufacturing System. The results demonstrate that both proposed variants significantly outperform both the FDO and the Modified Fitness Dependent Optimizer (MFDO) in solving these complex problems.
Keywords: Stochastic boundary, optimization, fitness dependent optimizer, Metaheuristic algorithm, Levy flight
Received: 03 Jun 2025; Accepted: 17 Sep 2025.
Copyright: © 2025 Faraj, Aladdin and Ameen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Aram Kamal Faraj, aram.kamal@chu.edu.iq
Aso M Aladdin, aso.aladdin@chu.edu.iq
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