Abstract: Aiming at the problem of finding the optimal path for mobile robots,a salp swarm algorithm BAGSSA(adaptive salp swarm algorithm with scale-free of BA network and golden sine algorithm) combining scale-free network,adaptive inertia weight and golden sine algorithm mutation strategy is proposed.First,a scale-free topology network is generated to map the relationship of followers,so as to enhance the global optimization ability of the algorithm;and the adaptive inertia weight is introduced in the followers to form a spontaneous adjustment to the overall distribution of the population and enhance the ability of local optimization.The variation of the golden sine algorithm is selected to further improve the accuracy of the solution.Secondly,through the simulation solution of 12 benchmark functions,experimental data show that the average value,standard deviation,Wilcoxon test and convergence curve are better than that ofthe standard SSA and other swarm intelligence algorithms.The proposed algorithmhas higher optimization accuracy and convergence speed.Finally,BAGSSA is applied to the path planning problem of mobile robots,and simulation experiments are carried out in two test environments.Simulation results show that the improved salp swarm algorithm is better than other algorithms in finding the path,and has certain theoretical and practical application value.

Key words: Salp swarm algorithm, Scale-free network, Self-adaption weighty, Golden sine algorithm, Route planning