Abstract: To solve the problem of slow convergence and susceptibility to local optima in solving robot path planning problems using the standard whale optimization algorithm (WOA)， an improved whale optimization algorithm (PSO-AWOA) with hybrid particle swarm algorithm and adaptive weights strategy is proposed. By introducing nonlinear inertia weight factors into the standard PSO and WOA algorithms and adaptively updating the weights during the population evolution， the global exploration ability and convergence speed are improved. Meanwhile， by introducing the PSO algorithm with strong optimization-seeking ability into the exploitation stage of the WOA algorithm， the new solution of iteration is guaranteed to be better than the original solution， which enhances the ability to jump out of the local optima. Finally， the PSO-AWOA algorithm is applied to generate the optimal path for the robot in the grid map simulation environment. The results show that the proposed PSO-AWOA algorithm outperforms in terms of optimization accuracy and convergence speed by comparing the time consumption， planning path length， and the number of turning points of the algorithms given， which verifies the effectiveness of the improved algorithm.

Key words: hybrid optimization algorithm; particle swarm optimization (PSO); whale optimization algorithm (WOA); adaptive weight; path planning