Abstract: In view of the shortcomings of the traditional pelican optimization algorithm， such as slow convergence speed and easy to fall into local optimal solutions， an improved pelican optimization algorithm based on Circle map initialization and adaptive t-distribution mutation is proposed. First， in the population initialization stage， the Circle mapping is used to generate an initial solution with a high degree of diversity， and combined with the reverse learning strategy， the diversity of the population is improved and the exploration ability of the population is enhanced. Secondly， in the iterative process， the adaptive t-distribution mutation operation is used to perturb the individual， which helps the pelican optimization algorithm jump out of the local optimal solution and improve the convergence speed. In addition， an adaptive factor and an improved inertia weight are introduced in the exploration stage of the pelican optimization algorithm， which better balances the global exploration ability and local development ability of the algorithm. Finally， IPOA is compared with other four classical algorithms on several test functions. Experimental results show that IPOA has a significant improvement in convergence speed， global search ability and convergence robustness.

Key words: pelican optimization algorithm, Circle mapping, adaptive factor, adaptive t-distribution mutation