Pelican optimization algorithm improved based on hybrid strategy

Aiming at the problems of low solving accuracy, insufficient stability, and easy to fall into local optimization of the pelican optimization algorithm, a improved pelican optimization algorithm (IPOA) with improved hybrid strategy is proposed. Firstly, in order to enhance the randomness and diversity of the population, expand the search range of the population, introduce the back-refraction learning mechanism. Secondly, the fusion of sine-cosine algorithm and pelican algorithm is used to improve the way of pelican search for prey, and enhance the local search and global search capabilities of the algorithm. Then, the Levy flight mechanism is used to update the position of the pelican, so as to improve the search ability of the algorithm to find the optimal value. Finally, an adaptive t-distribution variation operator is introduced, and the number of iterations of the algorithm is used as the degree-of-freedom parameter of the t-distribution to enhance the diversity of pelican populations and avoid the algorithm falling into local optimum. The improved algorithm is compared with the seagull optimization algorithm, chimpanzee optimization algorithm, whale optimization algorithm, snake swarm optimization algorithm, and basic pelican optimization algorithm through 12 standard test functions, and the results show that IPOA has better convergence speed and stability. Finally, the improved Pelican algorithm is applied to the pressure vessel design optimization problem, which further proves that the improved algorithm has good solution performance.