Table 1 Summary of existing works.

Hu et al. ⁴⁸

Enhanced hybrid AOA (CSOAOA)

Improves exploitation, avoids local optima, increases convergence accuracy

Imbalanced exploration-exploitation despite improved accuracy, potential for slow convergence in high-dimensional problems

Shen et al. ⁴⁹

Multi-population evolved WOA (MEWOA)

Increases convergence speed, avoids local optima, competitive performance

May face challenges in extremely high-dimensional problems despite improved convergence speed

Qiao et al. ⁵⁰

Hybrid AOA-HHO for MTIS

Improves segmentation accuracy, PSNR, SSIM, execution time

Focused on image segmentation, lacks general applicability across other domains

Qiu et al. ⁵¹

Improved Gray Wolf Optimization (IGWO)

Improves convergence speed, solution accuracy, escapes local minima

Requires fine-tuning to maintain performance across diverse problems, risk of local optima

Houssein et al. ⁵²

Improved Sooty Tern Optimization Algorithm (mSTOA)

Balances exploration/exploitation, avoids sub-optimal convergence

Control parameter sensitivity may lead to inconsistent performance in complex cases

Wu et al. ⁵³

Variant Ant Colony Optimization (MAACO)

Reduces path length, turn times, improves convergence speed

High computational cost for large-scale problems despite improved path planning performance

Nadimi-Shahraki et al. ⁵⁴

Enhanced Whale Optimization Algorithm (E-WOA)

Improves population diversity and search strategy

Struggles with maintaining balance in multi-objective tasks, relies heavily on parameter adjustment

Askr et al. ⁵⁵

Binary Enhanced Golden Jackal Optimization (BEGJO)

Boosts exploration and exploitation, outperforms in classification accuracy

Computationally expensive, may not generalize well to larger datasets despite classification improvements

Ozkaya et al. ⁵⁶

Adaptive Fitness-Distance Balance ARO (AFDB-ARO)

Balances exploration/exploitation, achieves optimal solutions

May struggle with large-scale problems despite performance in benchmark tests

Yıldız et al. ⁵⁷

Hybrid AOA-NM

Improves solution quality, avoids local optima traps

Limited applicability outside constrained design problems

Deng et al. ⁵⁸

Improved Whale Optimization Algorithm (IWOA)

Improves convergence speed, stability, accuracy

Challenges in dealing with complex constraints, potential slow convergence

Tan and Mohamad-Saleh ⁵⁹

Hybrid Equilibrium Whale Optimization Algorithm (EWOA)

Superior statistical performance, convergence rate, robustness

Improved robustness but limited efficiency in more complex, high-dimensional spaces

Mahajan et al. ⁶⁰

Hybrid AO-AOA

Effective in high- and low-dimensional problems

limited exploration in certain complex tasks

Qian et al. ⁶¹

Hybrid SSACO

Avoids local optima, improves convergence accuracy

Limited exploration capabilities in high-dimensional, non-convex problems

Zhu et al. ⁶²

Enhanced Dung Beetle Optimization (QHDBO)

Improves convergence speed, accuracy, robustness

Still prone to local optima in extremely challenging problems despite overall improvements