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Open Access

REVIEW

Industrial-Oriented Applications of Sparrow Search Algorithm in Machine Learning Optimization: A Review of Emerging Trends

Linhui Wang1,2, Mohd Khair Hassan1,*, Ghulam E Mustafa Abro3,*, Mehrullah Soomro1, Hifza Mustafa4
1 Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Jalan Universiti 1, Serdang, Selangor, Malaysia
2 School of Mechanical and Vehicle Engineering, Nanchang Institute of Science & Technology, Nanchang, China
3 Artificial Intelligence in Robotics Laboratory (AiR Lab), Electrical and Computer Engineering Department, Aarhus University, Aarhus C, Denmark
4 Department of Applied Sciences (DAS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia
* Corresponding Author: Mohd Khair Hassan. Email: email; Ghulam E Mustafa Abro. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.074207

Received 05 October 2025; Accepted 27 November 2025; Published online 18 March 2026

Abstract

Industrial intelligent systems increasingly require efficient, robust, and deployable optimization methods for resource-constrained hardware. The Sparrow Search Algorithm (SSA) has gained traction in machine learning optimization; however, existing reviews emphasize algorithmic variants and generic benchmarks while paying limited attention to industrial requirements such as real-time operation, noise tolerance, and hardware awareness. This review advances the field by developing an industrial taxonomy that aligns SSA and its hybrids with six application clusters—fault diagnosis, production scheduling, edge-intelligent control, renewable/microgrid optimization, battery prognostics, and industrial cybersecurity—characterizing task types, data regimes, latency and safety constraints, and typical failure modes; by consolidating a benchmark evidence base that compiles representative datasets, metrics, compute budgets, baseline line-ups (PSO/GA/DE/GWO), and anytime behavior (time-to-target, AUC-anytime) for fair, reproducible comparison; and by distilling practitioner-oriented guidance that includes a variant-selection matrix (e.g., quantum/DE hybrids for high-dimensional tuning, chaotic/Lévy SSA for noisy multimodal landscapes, multi-objective SSA for trade-off-intensive scheduling), robust default hyper-ranges, and a deployment checklist covering robustness tests, calibration and explainability, and latency/energy reporting under edge constraints. Comparative evidence across non-convex, high-dimensional, and noise-aware tasks identifies conditions under which SSA and its hybrids surpass classical optimizers, alongside analyses of scalability and real-time feasibility, and articulates the remaining challenges and research directions to support rigorous benchmarking and trustworthy industrial deployment.

Keywords

SSA; industrial optimization; machine learning; smart manufacturing; benchmark synthesis; hyperphysical systems

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