A hybrid multi-strategy SCSO algorithm for robot path planning

To address the problems of low convergence efficiency and the tendency to fall into local extremes in the sand cat swarm optimization algorithm for solving the path planning problem of mobile robots, a hybrid multi-strategy sand cat swarm optimization (HMSCSO) algorithm is proposed. Firstly, a non-linear adjustment strategy is used to increase the convergence accuracy of the algorithm. Then, the logarithmic weight strategy is introduced into the position update to balance the exploration and exploitation ability of the algorithm. Next, the alternate selection strategy is used to improve the algorithm's ability to jump out of local extremes. Finally, the L & eacute;vy flight position update formula is introduced into the algorithm to alleviate the situation where the algorithm falls into stagnation. To verify the effectiveness of the proposed HMSCSO algorithm, 23 benchmark test functions and CEC2022 test functions are selected for comparison with other advanced optimizers. In addition, the HMSCSO algorithm is subjected to ablation experiments in three groups of environments with different obstacles. The experimental results show that after 30 independent experiments, the average path length of the HMSCSO algorithm in path planning is shortened by 23.30%, 2.32%, and 30.20% compared to the original algorithm in three different environments, respectively, with a maximum shortening of 37.73%, 55.75%, and 85.28% compared to other algorithms in the same environments.