In order to solve the problem of premature convergence of traditional particle swarm optimization (PSO) algorithm in path planning, which is easy to fall into local optimal solution and poor path quality, this paper proposes the corresponding PSO algorithm of simulated annealing optimization. When planning the path of mobile robot, it analyzes the effect of initial temperature and cooling coefficient on path length and iteration times in the main parameters of simulated annealing algorithm. And deduce the law of its change and seek the optimal parameter matching. Simulated annealing algorithm can not only move the updated particle position according to the particle swarm optimization formula, but also select the updated position with a certain probability, so as to avoid the particle falling into the local optimal solution in the whole iterative process, and improve the global optimization ability. The simulation results show that compared with the traditional PSO algorithm, the simulated annealing PSO algorithm in complex environment has better optimization ability, shorter path and fewer iterations.
Authors: Jie Zhao (Heilongjiang University of Science and Technology), Xuesong Sheng (Heilongjiang University of Science and Technology), Jianghao Shi (Heilongjiang University of Science and Technology),
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