Aiming at the shortcomings of the original Aquila Optimizer （AO）， such as insufficient local development ability， low optimization accuracy and slow convergence speed， a Multi-Strategy Improved AO （MSIAO） for robot path planning was proposed. Firstly， the Sobol sequence was introduced to initialize the Aquila population， which was conducive to diversity of the initial population and improved the convergence speed. Secondly， the local search method was improved by using golden sine operator and idea of self-learning and social learning of particle swarm， which enhanced exploitation ability of the algorithm and reduced the possibility of falling into the local optimum. Meanwhile， a non-linear balance factor was used as switching condition of the two stages， which made better communication among the populations， and was able to balance the global exploration and local exploitation more effectively. Finally， multiple experiments were carried out. Through the simulation on 12 benchmark functions and 10 CEC2017 complex functions， it can be seen that the proposed improvement strategies enhance the global optimization ability of MSIAO greatly. Results of applying MSIAO to robot path planning show that MSIAO can obtain shorter and more reliable moving paths. In 20×20 grid map， the average path of MSIAO is shortened by 2.53%， 3.83%， and 6.70% compared to those of Particle Swarm Optimization （PSO） algorithm， the original AO， and Butterfly Optimization Algorithm （BOA）， respectively； and in 40×40 grid map， the average path of MSIAO is shortened by 10.65%， 5.27%， and 14.88% compared to those of the above three algorithms， verifying that the path-finding of MSIAO is more efficient.
