*3.3. Proposed Path Planning and Tracking Optimization Algorithm*

In recent years, many metaheuristic optimization algorithms that imitate living things in nature have been used extensively to solve complex nonlinear engineering problems. These algorithms stand out compared to traditional optimization techniques such as stochastic and deterministic approaches, with their flexibility, simplicity, avoidance of local optima, and ability to search randomly. In this study, in order to overcome the problem of planning the optimum path and tracking this path for the quadcopter, a swarm-based hybrid optimization approach is proposed, which contains GWO and HHO [45] algorithms and has high convergence speed and is capable of avoiding local minima. The proposed optimization algorithm allows the quadcopter to not only avoid obstacles but also to follow the planned path for payload holding-releasing with minimum error. The performance of the proposed algorithm is compared with PSO and GWO algorithms. The PSO, GWO, and hybrid GWO–HHO algorithms used for the quadcopter's path planning and tracking are described in this section.
