## 3 Review of Path-Planning Techniques for Data Harvesting
### Meta-Heuristic Methods  
Meta-heuristic methods draw inspiration from natural and social phenomena, often imitating the behavior of biological process or simulating collective behaviors observed in nature. These methods, which include GA, ACO, PSO, SA, and many others, operate by iterative refining and evolving potential solutions over generations or iterations. By integrating randomness and probabilistic components, they navigate the solution space in a manner that balances the exploration and exploitation. Due to their ability to handle complex, multi-dimensional, and non-convex optimization problems, meta-heuristic methods find extensive application in diverse domains such as engineering [110], economics [111], and, as mentioned earlier, in path planning for intricate tasks like ocean data collection.  
Due to the inherent capacity to manage intricate problems, there has been a growing trend towards the utilization of meta-heuristic algorithms in oceanic data collection. In support of the trajectory planning procedure, a genetic algorithm-driven approach was formulated in the work [96] to determine the optimal resurfacing schedule. The optimization criteria employed include the value of information (VoI), which captures the time-critical nature of gathered data. Addressing the non-holonomic restrictions of the AUVs, the study presented in [95] introduced a 3D path-planning model based on Dubins curves. This model, solved using a genetic algorithm, proposes a continuous path configuration that eliminates abrupt direction changes at sensor nodes. Subsequently, building upon the concepts in [95], the authors of [94] expanded this research by incorporating considerations for energy efficiency. This was achieved by introducing continuous Bezier curves in the Z-axis, building upon the 2D Dubins curves framework. In order to achieve enhanced energy efficiency while accounting for mobility factors, the study conducted in [92] introduces a dynamic clustering algorithm independent of location and a genetic-driven methodology. In comparison to conventional approaches, the solution results in a network lifetime improvement of twofold, a nearly 30% reduction in total energy consumption, and a 15% increase in delivery ratio under conditions of low densities. In the context of energy management within nodes and angle control, the research in [93] presents an energy-conscious clustering protocol and a novel ACO algorithm that integrates with a Markov Reward Process (R-ACO). This integration is employed to optimize both distance and angle considerations in path planning. Addressing the dual goals of balancing energy consumption and network throughput, the authors of [32] divided the optimization challenge into four distinct segments. To find the optimal solution, they harnessed the power of ant colony optimization techniques. Furthermore, the communication range has been taken into account by research in [91]. Their contribution includes the proposal of an AUV-assisted data collection approach centered on Fermat's spiral (FS-PPS). This method employs an enhanced firefly algorithm to ascertain the sequence in which nodes are traversed. In the context of addressing challenges like imbalanced energy consumption, prolonged delays, and partial coverage, the study outlined in [89] employs the backtracking search optimization (BSO) technique. This is utilized to attain an optimal selection of coverage-aware target nodes and to devise trajectories for multiple AUVs. Introducing a heterogeneous USV-AUV system to the data collection, the authors of [87] introduced a modified shuffled frog-leaping algorithm (SFLA). This alteration is applied to optimize the coordination schedule between USVs and multiple AUVs.