Optimization Algorithm of Spotted Hyena Based on Chaotic Reverse Learning Strategy

The application of swarm optimization algorithm in WSNs has become a new research hotspot of scholars at home and abroad. Aiming at the problem that the spotted hyena optimization algorithm is easy to fall into local optimum, which leads to low optimization accuracy, an improved spotted hyena optimization algorithm is proposed. On the basis of the original algorithm, Sine chaotic map and elite reverse learning strategy are embedded to reduce the probability of falling into local optimum and improve the global search ability of spotted hyena optimization algorithm. In addition, the adaptive inertia weight is introduced to balance the global search and local development capabilities of the spotted hyena optimization algorithm. The experimental results show that compared with the original spotted hyena optimization algorithm, sine and cosine algorithm, multiverse optimization algorithm, differential evolution algorithm and particle swarm optimization algorithm, the improved algorithm has significant performance advantages in optimization ability and stability.