Optimal Mobile Node Path Optimization Based on Ant Colony Algorithm

With the progress of science and technology and artificial intelligence, path planning and optimal path solving have gradually become hot topics. Considering the frequent deadlock problem of ant colony algorithm in path planning problem, and the long running time and low computational efficiency of ant colony algorithm. The research of node ant colony technology is based on Darwin's theory of evolution. To begin, based on the previous research on ant colony algorithm and the model learning of path search, a simple ant colony algorithm model is proposed to deal with the blind search problem in the blank grid. When the goal influence factor and the barrier impact factor are combined into the heuristic function, the global search capabilities increase and impasse is averted. The next phase is to use Darwin's theory of evolution to improve the pheromone workup of the ant colonies algorithm in order to speed up and lower the predicted time cost. Experiments on raster maps of various sizes show that the evolutionary ant colony technique suggested in this study boosts iteration speed, improves search efficiency, finds the best path, and avoids stalemate.