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Bionic algorithms have always played an important role in industrial, agricultural, and scientific research. The optimization of bionics algorithms has always been the focus of scholars in various countries. A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm. The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population. Optimizing the adaptive convergence factor is able to balance the local and global optimization ability. The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability. In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm. In the simulation, IWOA, Ant Colony Optimization, Particle Swarm Optimization, Whale Optimization Algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions. The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.

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