为了解决传统人工鱼群算法在求解旅行商问题时遇到寻优精度低、算法收敛慢、易于落入局部最优解等问题，提出了一种融合交叉变异的改进人工鱼群算法。首先，通过在鱼群迭代求解过程中加入交叉变异操作，增强种群的多样性方式，提高算法在全局搜索中寻找更优解的能力；其次，引入自适应的人工鱼群策略，动态调整视野距离与拥挤度因子，从而改善算法的局部探索能力与收敛速度；最后，在MATLAB环境下使用TSPLIB数据集进行仿真验证。结果表明，改进后的人工鱼群算法在收敛速度和寻优精度方面较传统方法有显著提升，跳出局部最优解的能力得到增强，路径规划结果更接近最优解；最后，进一步对经典旅行商问题模型的地图维度和路径进行改进，最终实现本文改进算法在三维多点覆盖路径规划上的应用。

To address the issues of low optimization accuracy, slow convergence speed, and susceptibility to local optima encountered by traditional artificial fish swarm algorithm (AFSA) when solving the traveling salesman problem (TSP), an improved AFSA algorithm integrated with cross-over mutation was improved. Firstly, by introducing cross-over mutation operations during the iterative solving process of the fish swarm, population diversity was enhanced, thereby improving the algorithm's capability to find better solutions in global search. Secondly, an adaptive fish swarm strategy was introduced, dynamically adjusting the visual range and crowding factor to enhance the algorithm's local exploration capability and convergence speed. Thirdly, simulation verification was conducted using the TSPLIB dataset in the MATLAB environment. Results demonstrate that the improved AFSA algorithm exhibits significant improvements in convergence speed and optimization accuracy compared to traditional methods, with enhanced ability to escape local optima and path planning results closer to the optimal solution. Finally, further improvements were made to the classical TSP model in terms of map dimensions and paths, ultimately realizing the application of this improved algorithm in three-dimensional multi-point coverage path planning.