A UAV path planning method based on the framework of multi-objective jellyfish search algorithm.

Multi-constraint UAV path planning problems can be viewed as many-objective optimization problems that can be solved by meta-heuristic algorithms with good self-organizing optimization capabilities. However, such algorithms mostly use random initializing methods, resulting in low-quality initial paths that reduce the efficiency of subsequent algorithmic searches. Moreover, as the number of objective functions increases, meta-heuristic algorithms face inadequate selection pressure and convergence capability, which lead to poor solution. In order to address these issues, this paper proposes a UAV path planning method based on the framework of multi-objective jellyfish search algorithm (UMOJS). Firstly, an initializing strategy based on Rapidly-exploring Random Trees (RRT) is proposed to achieve higher quality initial paths. Secondly, a jellyfish updating strategy guided by the class-optimal individual is designed to enhance the convergence ability of the algorithm. Furthermore, a set of predefined reference points is imported to obtain Pareto optimal solutions with better convergence and distribution in many-objective optimization problems. To evaluate the superiority of the proposed UMOJS algorithm, three different difficulties of simulated flight environments are constructed to verify its performance. The experimental results show that UMOJS is not only able to gain more UAV paths with shorter length, but also more evenly distributed Pareto optimal solutions compared to five meta-heuristic algorithms when the constraint conditions are satisfied.