Abstract: In the face of complex optimization problems, the basic whale optimization algorithm still has problems such as easy to fall into local extremum, slow convergence speed and low calculation accuracy. Therefore, a whale optimization algorithm based on teaching and learning and dimensional Cauchy mutation, named TCWOA, is proposed. Firstly, Sobol sequence is selected to initialize the whale population, which can make the population distribution more uniform. Secondly, the teaching strategy in the teaching and learning algorithm is introduced to replace the random search strategy, avoiding the blindness of search and improving its convergence speed. Thirdly, the dimensional Cauchy mutation with inertia weight is used to perturb the whale's optimal individual, which can make it jump out of the local optimal solution and enhance the global search ability of the algorithm. Finally, the comparative analysis of various optimization algorithms on 10 standard test functions ware carried out. In the application research, TCWOA algorithm first optimize BP network parameters and then predicts Boston housing prices. The results verify the effectiveness and accuracy of the optimization algorithm. 

Key words: whale optimization algorithm, teaching and learning algorithm, dimensional Cauchy mutation, TCWOA-BP model prediction