Augmenting Particle Swarm Optimization with Simulated Annealing and Dimensional Learning for UAVs Path Planning

In order to mitigate premature convergence commonly faced by conventional Particle Swarm Optimization (PSO) and enhance the algorithm's global search cparticles can swiftly navigatingapability in UAV path planning, this paper proposes a simulated annealing and dimensional learning augmented particle swarm optimization algorithm (SDPSO). Firstly, learning factors and inertia weights are dynamically adjusted in the search process to achieve a balance between global exploration and local exploitation. Subsequently, the simulated annealing (SA) algorithm is utilized in the early search phase to help the algorithm escape from local optima and enhance its ability to discover the global optimal solution, while retaining the fast convergence characteristic of PSO. Moreover, to rectify the challenge of particle oscillation appeared in the search process, SDPSO embeds a dimensional learning strategy (DLS), which enables all dimensions of each particle to learn useful information from the global optimal. Experimental results demonstrate that incorporating SA in the first 30 iterations of the algorithm not only enhance the capability of jumping out from local optima, but also maintains the rapid convergence characteristic of PSO. Comparative experiments conducted in two distinct environments reveal that SDPSO exhibits advantages in terms of optimization capability, convergence rate, and robustness when compared to other algorithms.