Improved dragonfly algorithm based on chaos mapping and differential evolution

An improved dragonfly algorithm based on chaotic mapping and differential evolution was proposed to address several issues encountered in the original algorithm. The initial population’s significant randomness and the difficulty in adjusting algorithmic weight parameters led to low convergence accuracy. Additionally, later-stage population contraction restrictions resulted in decreased vitality and slow convergence speed. By employing Tent chaotic mapping, the population’s initial distribution was made more uniform. The alignment, clustering, and inertia weights were adjusted to enhance convergence speed and accuracy. The introduction of the differential evolution algorithm aimed to accelerate convergence at the final stages. Finally, nine test functions were selected for comparative simulation experiments. The results demonstrated that, compared to the basic dragonfly algorithm and the differential evolution dragonfly algorithm, the improved dragonfly algorithm based on chaotic mapping and differential evolution has significantly improved convergence speed and accuracy, avoiding getting stuck in local optima and obtaining stable and reliable global optimal solutions.