Abstract: In order to solve the problems of the original whale optimization algorithm (WOA) with slow convergence speed, weak global search ability, low solution accuracy and easy to fall into local optimization, a hybrid strategy is proposed to improve the whale optimization algorithm (LGWOA). Firstly, the Levy flight strategy is introduced into the position update formula of the whale random search, and the global search step is increased through Levy flight, the search space is enlarged, and the global search capability is improved. Secondly, the adaptive weight is introduced into the whale spiral upward position update formula to improve the algorithm’s local search ability and optimization accuracy. Finally, the idea combining the genetic algorithm’s cross mutation is used to balance the algorithm’s global search and local search capabilities, maintain the diversity of the population, and avoid falling into the local optimum. Simulation experiments on 12 benchmark test functions in different dimensions show that the improved whale algorithm has faster convergence speed and higher optimization accuracy.

Key words: whale optimization algorithm, Levy flight, cross variation, adaptive weight, function optimization