Traditional path planning algorithms have problems such as low efficiency, easy to fall into local optimal solutions, low convergence accuracy, etc. The subtractive average optimization algorithm has fewer parameters and simpler principles than other algorithms, but it ignores the influence of optimal values during the search process, which causes the algorithm to fall into local optimal solutions. Aiming at this problem, this paper proposes a subtractive average optimization algorithm incorporating multi-strategy improvement for path planning. First of all, Tent chaotic mapping is used to initialize the search agent population to ensure the diversity of the population; an adaptive guidance mechanism is introduced to enable the algorithm to adaptively choose a better update method with the number of iterations; the population update strategy of the sine-cosine algorithm is integrated into the update method of the search agent, and the good fluctuating and oscillating nature of the sine-cosine algorithm is utilized to balance the global and local searches of the algorithm and to better ensure the algorithm′s convergence accuracy. Finally, the proposed algorithm is simulated and tested by choosing seven benchmark test functions and setting different raster map environments. The results show that the proposed algorithm has better convergence accuracy and speed, and the performance index of path planning is better and the planning effect is better.