Volume 7 • Issue 2 • PP: 01–06 • 2024
Exploring Optimization Algorithms: A Review of Methods and Applications
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This review focuses on feature selection as an important parameter in tuning machine-learning models. It surveys optimization algorithms including Moth-Flame Optimization (MFO), the hybrid GA–GSA algorithm, Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Henry Gas Solubility Optimization (HGSO), and safety-constrained Bayesian optimization. These algorithms are used in engineering design, power-system scheduling, and other application areas. The review emphasizes optimization’s role in improving efficiency, reducing errors, and increasing model interpretability, and examines applications in structural engineering, additive manufacturing, and landslide-susceptibility mapping. A summary table presents the studies, methods, and key findings. The reviewed work demonstrates the adaptability and competitiveness of optimization algorithms across diverse domains and aims to guide researchers and practitioners by describing current approaches and directions for future research.
Keywords
References
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