Table 21.

Comparison of recent optimization techniques (2023–2025).

Optimizer/study	Year	Key idea/mechanism	Strengths	Limitations (why not used here)	Reference
Adaptive-mechanism GWO (AM-GWO)	2025	Introduces adaptive exploration–exploitation balancing with dynamic control parameters	Excellent performance for high-dimensional feature selection; strong convergence	Higher computational load; requires fine parameter tuning; best suited for offline / GPU-supported tasks	J. Wang et al.58
Adaptive-weight GWO (AWGWO)	2024	Weight-adjusted wolf hierarchy to enhance search diversity	Effective for security domains (e.g., malware detection); improves convergence	Increased hyperparameters; slower iteration time; unsuitable for browser-side deployment	Zhang, Y., & Cai, Y59.
EGSO-CNN deep-learning optimizer	2025	Evolutionary optimizer integrated with CNN-based phishing detection	Very high accuracy; robust phishing URL detection	Requires DL models + GPU; unsuitable for lightweight real-time inference	Ragab, M., et al.60
Firefly–GWO hybrid	2024	Combines swarm intelligence (firefly) with GWO for improved feature search	Good results in phishing detection; strong global search	Computationally expensive; hybrid models unsuitable for in-browser execution	O. S. Qasim et al.61
Gradient-optimized TSK Fuzzy Model	2025	Uses optimization-driven fuzzy rules for explainable phishing detection	Highly explainable; competitive performance	Model complexity; larger memory footprint; not feasible for extension-based deployment	M. Sieverding, N. Steffen, and K. Cohen62
Standard binary GWO (used in this work)	–	Simple binary position encoding with alpha–beta–delta leadership	Lightweight, fast, low memory; stable with mixed URL + visual features; ideal for browser extensions	Slightly lower accuracy than advanced hybrids in offline tests	This paper