A hybrid multi-strategy SCSO algorithm for robot path planning

To address the problems of low convergence efficiency and easy 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 based sand cat swarm optimization (HMSCSO) algorithm is proposed to solve the path planning problem of mobile robots. First, the nonlinear 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 ability of the algorithm to jump out of local extremes; Finally, the position update formula of Lévy flight is introduced into the algorithm to alleviate the situation that 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 the HMSCSO algorithm can effectively improve the solution accuracy.