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.
Wang et al. (2026) studied this question.