Abstract: A whale optimization algorithm based on Levy flight and Brownian motion is designed to overcome the disadvantages of whale optimization algorithm such as low accuracy,slow convergence and easiness to fall into local optimum.First,Levy flight method is used to initialize the whale population to increase the diversity of the initial population.Then,according to the principle of Brownian motion,the position update of the whale population is randomly perturbed to avoid the algorithm falling into local optimization in advance.The improved whale optimization algorithm is compared with whale optimization algorithm,particle swarm optimization algorithm,genetic algorithm and ant colony optimization algorithm on seven different benchmark test functions.The experimental results show that the improved whale optimization algorithm is superior to the other four algorithms in terms of solution accuracy and convergence speed.At the same time,simulation and comparison experiments are carried out on the initial solution exploration range of the improved whale optimization algorithm using Levy flight strategy and the whale optimization algorithm using random search strategy in the initialization stage.The experimental results show that the improved whale optimization algorithm can avoid falling into local optimization in a certain program.

Key words: swarm intelligence, whale optimization algorithm, Levy flight, Brownian motion