Table 2.
Limitations of the reviewed existing swarm intelligent metaheuristic optimization algorithms-based path planning mechanisms
| Author | Mechanism used | Limitations |
|---|---|---|
| Liu et al.23 | Evolutionary algorithm based dynamic path planning Mechanism | The path determination cost and computational complexity needs significant improvement since the time used for covering all regions need to be speeding up |
| Flores-Caballero et al.27 | Meta-heuristic A-Star method | The fine tuning of parameters used for fitness function evaluation needs potential improvement, since the weighting coefficient in complex scenarios are not reactive |
| Ait-Saadi et al.24 | Hybrid Simulated Annealing and Chaotic Aquilla Optimization Algorithm (HSACAOA)-based coverage path planning mechanism | The uncertainty involved in determining the candidate solutions need to be handled with maximized balance such that multiple regions are enveloped with the shortest time of flight |
| Chen et al.31 | Ant Colony Optimization Algorithm-based Coverage Path Planning Mechanism (ACOACPPM) | The linear programming model needs improvement in satisfying the constraints essential in-flight path optimization process |
| Aslan et al.32 | Multi-Population-based Immune Plasma Algorithm (MPIPA) | The concept of multi-population even through helps in determining potential number of candidate solutions, still need to maximize its solution diversity and prevent premature convergence |
| Zhang et al.30 | An Improved Artificial fish swarm algorithm-based path planning mechanism (AFSAPPM) | The performance offered by the included algorithm for path planning needs improvement in determining optimal solutions independent to the number of candidate solutions determined in the search space |
| Xu et al.26 | Random Tree Star Metaheuristic Algorithm (RTSMA) | The sampling method used for exploration need to be improved, and balance between exploitation and exploration needs significant enhancement |
| Tan et al.28 | Game Theory Model Using PSO (GTMPSO) for optimal path planning | The process of achieving Nash equilibrium depending on the number of players included for path planning may incur a comparatively high time and computational complexity |
| You et al.25 | UAV planning approach using multi-strategy enhanced Harris Hawk Optimization Algorithm (MSEHHOA) | The time involved during the process of path planning was comparatively high, and hence there exists a scope of enhancement in reducing the time of UAVs flight |
| Liu et al.29 | PSO-based spherical vector-based adaptive evolutionary PSO algorithm | The balance established between the rate of exploitation and exploration during the process of path planning is marginal and needs high degree of accuracy in UAVs flight for target achievement |
| Hu et al.33 | ACO and Said-Ball curve-based UAVs’ path planning approach | The convergence speed of this path planning algorithm is moderate and hence needs significant improvement in achieving the required degree of tasks |