Research on Path Planning Based on Improved Slime Mold Algorithm

The basic slime mold algorithm tends to become trapped in local optima during the optimization process, and the convergence rate is slow, so an improved slime mold algorithm is used. Firstly, a low difference sequence Halton was used to initialize the population, so that the distribution of slime molds in the initial population was uniform and non-overlapping. Secondly, adaptive parameters with more reasonable value changes and Levy flight are introduced into the position updating process, so that the algorithm has a reduced tendency to become trapped in local optima. Finally, six test functions are used to compare the improved slime mold algorithm with the basic slime mold algorithm, particle swarm optimization algorithm and whale algorithm, and it can be observed that the improved slime mold algorithm exhibits a higher convergence rate and greater capacity to escape local optima. Then, the improved slime mold algorithm is applied to grid path planning. By comparing the path length and iteration curve, it has been demonstrated that the improved slime mold algorithm can be utilized more effectively for path planning.