LAUSR

To address the defects of the fast convergence speed of the lion swarm algorithm and ease of falling into a local optimum, a variable-speed elastic collision lion swarm algorithm (VELSO) is proposed. First, the flexibility of the lioness search is increased according to the variable spiral search strategy, and then the two lionesses learn learning strategies of teaching and learning algorithms to enhance the interactive behavior of lioness hunting. Then, the improved refraction reverse learning strategy was used to increase the diversity of the population and make the individual quality of the population. Finally, the variable-speed elastic collision strategy is used to increase the probability of the algorithm jumping out of the local optimum and to improve the ability of the algorithm to obtain the optimal solution. To verify the effectiveness of the proposed algorithm, 16 test functions were used to test the proposed algorithm and compared with other algorithms, which proved that the proposed algorithm was very effective. Finally, the proposed algorithm is applied to DV-Hop positioning, which verifies the feasibility and practicability of the proposed algorithm.