MODMOA: A Novel Multi-objective Optimization Algorithm for Unmanned Aerial Vehicle Path Planning

Multi-Objective UAV path planning problem is to find an optimal path, which can satisfy multiple objectives at the same time and optimize other performance indicators in the case of considering multiple conflicting objectives, constraints and trade-offs. In order to solve the challenge of considering multiple constraints and responding to environmental changes in real time, we propose a UAV path planning method based on multi-objective dwarf mongoose optimization algorithm. Considering the general search efficiency and insufficient global convergence of the original algorithms, nonlinear factorial augmented search strategies, chaotic mapping, and on-the-fly search strategies have been proposed to address the above problems and to enhance the algorithmic population diversity and local optimization capability. Experimental results show that, compared with other algorithms, the proposed method has better performance and robustness in multi-target UAV path planning, and can effectively find high-quality non-inferior solution sets, which provides an effective solution for UAV path planning.