基于改进的复合自适应遗传算法的 UUV 水下回收路径规划

Mutations of traditional genetic algorithms generate new paths in a simple and random manner, which negatively influence the evolutionary performance of the algorithms and makes it easy for them to fall into the trap of local optimality. Moreover, genetic algorithms are usually used together with the grid method for path planning, and the optimal path obtained is not always the shortest path for UUV recovery path planning, and the UUV mobility performance might conflict with the optimal path. An improved genetic algorithm with UUV mobility constraints is thus proposed. The concept of environment complexity is proposed to analyze the specific value of mobility constraints, so that path planning can be adapted to UUV mobility, and the algorithm results can be more practical. The compound adaptive mutation strategy is proposed to control the adaptive evolution of the mutated individuals in the iterative process. When the population evolution stagnates after a certain number of iterations, the optimal individual is guided for a two-stage adaptive mutation so that the optimal path approaches the approximate global optimal solution, and the convergence rate of the algorithm is effectively improved. The algorithm comparison simulation results based on MATLAB software show that the optimal path generated by the improved compound adaptive genetic algorithm is smoother and shorter in length compared with the optimal path of genetic algorithm and adaptive genetic algorithm in generally complex water area and complex water area, which demonstrates that the improved compound adaptive genetic algorithm has better convergence performance and superiority seeking ability in path planning and is more feasible and superior.