Solving Traveling Salesman Problem and Multi-point Path Planning Based on Improved Artificial Fish Swarm Algorithm
To address the issues of low optimization accuracy,slow convergence speed,and susceptibility to local optima encoun-tered by traditional artificial fish swarm algorithm(AFSA)when solving the traveling salesman problem(TSP),an improved AFSA al-gorithm 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 solu-tions 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 im-provements in convergence speed and optimization accuracy compared to traditional methods,with enhanced ability to escape local opti-ma 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.
artificial fish swarm algorithmtraveling salesman problemcrossover and mutationadaptive vision rangepath plan-ning
万方数据
维普
