Table 1.
Review of the existing swarm intelligent metaheuristic optimization algorithms-based path planning mechanisms
| Author | Mechanism used | Merits |
|---|---|---|
| Liu et al.23 | Evolutionary algorithm based dynamic path planning Mechanism | It addressed the problem of low search efficiency and high computational complexity during the process of determining optimal paths between the source and destination paths in the implementation environment |
| Flores-Caballero et al.27 | Meta-heuristic A-Star method | This metaheuristic approach determined the firsts set of candidate solutions that suitably adjusted the control paths to identify the path between the source and destination scan points associated with a region |
| Ait-Saadi et al.24 | Hybrid Simulated Annealing and Chaotic Aquilla Optimization Algorithm (HSACAOA)-based coverage path planning mechanism | It ensured Collison-free, smooth and short path between the source and destination points, and it specifically adopted the characteristics of simulated annealing for establishing better balance between the exploration and exploitation rate |
| Chen et al.31 | Ant Colony Optimization Algorithm-based Coverage Path Planning Mechanism (ACOACPPM) | It initially adopted the UAVs models and constructed the regions such that linear programming-based problem can be modelled for establishing the best point-to-point path of flight between each of the individual UAVs |
| Aslan et al.32 | Multi-Population-based Immune Plasma Algorithm (MPIPA) | It adopted the remarkable properties of MPIPA for identifying the fuel potential paths that helps in achieving more possible candidate solutions which can be identified in least amount of time |
| Zhang et al.30 | An Improved Artificial fish swarm algorithm-based path planning mechanism (AFSAPPM) | This path planning mechanism used an improved AFSA algorithm for preventing the problem related to local optimization susceptibility and non-uniform population distribution |
| Xu et al.26 | Random Tree Star Metaheuristic Algorithm (RTSMA) | This path planning approach which included the merits of bias sampling and asymptotic optimality aided in better exploration of search space to support the flight of heterogeneous UAVs in the network |
| Tan et al.28 | Game Theory Model Using PSO (GTMPSO) for optimal path planning | It handled the challenges that are more common in complex environments during the process of determining optimal flight paths, and established better balance between the UAVs exploitation and exploration capabilities by adopting a game theory-based Nash Equilibrium |
| You et al.25 | UAV planning approach using multi-strategy enhanced Harris Hawk Optimization Algorithm (MSEHHOA) |
This MSEHHO approach initially used the merits of double adaptive weight methodology which improved the speed of path planning with improved convergence accuracy It was proposed with the objective of balancing the trade-off between the rate of exploration and exploitation with sustained solution diversity through the employment of dimension learning-based hunting methodology |
| Liu et al.29 | PSO-based spherical vector-based adaptive evolutionary PSO algorithm |
This PSO-based path planning specifically used the merits of reverse learning and tent map for proper guiding of the paths that the UAVs need to follow for better accomplishment of tasks It was proposed with the capability of distributing the initial solutions and enhancing the diversity of the solutions in the search space |
| Hu et al.33 | ACO and Said-Ball curve-based UAVs’ path planning approach |
This ACO-SBC approach used the primitive principles of intelligent metaheuristic algorithms for continuous monitoring the movement of UAVs in the network It used the merits of adaptive foraging strategy which optimizes the potential of ACO towards the process of balancing the phases of exploration and exploitation in the search space |