Mixed strategy improves dwarf mongoose optimization algorithm
Xiaogang Liu, Wenbo Gao · 2023
Aiming at the problems of dwarf mongoose optimization algorithm with weak optimization ability and slow convergence speed, an improved dwarf mongoose optimization algorithm is proposed. Firstly, the dwarf mongoose optimization algorithm performs secondary reverse learning solution on elite individuals, and selects the best individuals to form a new population to improve the initial population diversity. Then, combined with the sparrow position update strategy in the Sparrow Search Algorithm, the dwarf mongoose location update method is improved to improve the local development ability of the algorithm. Then, the Gaussian mutation strategy is used to improve the algorithm's optimization ability and convergence speed. Finally, eight benchmark functions are compared with four different types of algorithms and three DMO algorithms with individual strategies, and the results show that the improved dwarf mongoose optimization algorithm has higher optimization accuracy, convergence speed and stability.