Hybrid Path Planning Based on Golden Jackal Optimization and Particle Swarm Optimization

To address the issues of the golden jackal optimization (GJO) algorithm—such as susceptibility to local optima, poor convergence, and non-smooth paths—in mobile robot path planning, this paper proposes an improved GJO algorithm integrated with a particle swarm optimization strategy (PSO-GJO). First, a gaussian mutation strategy is introduced to probabilistically perturb the position of the current optimal solution. Combined with a greedy selection mechanism, this retains superior solutions and balances the algorithm’s exploration and exploitation capabilities. Second, by incorporating particle swarm optimization principles, the position update formula is redesigned to dynamically weight collective information and individual historical optima, thereby enhancing global search efficiency. Simulation experiments in grid environments demonstrate that the improved algorithm achieves faster convergence, stronger search capability, smoother paths, and higher stability in mobile robot path planning.