Momentum multi-objective optimization algorithm based on black hole algorithm

Abstract

In this paper, we mainly study the application of a multi-objective optimization algorithm based on the black hole algorithm in motor optimization. Using the combination of global search and local search, the continuous search area is better. The random search method is used in the global search, and a momentum gradient method is used in the local search, which makes the search results have faster convergence rates and easier convergence to the global optimal. A new file management strategy is used to make the optimization results more global and have better generalization ability. in practical application, the design of motor is often affected by manufacturing error, so random noise is added to the selection of optimization results, which can be more in line with the practical application. Finally, the actual motor model and complex function are used to test the performance of the optimization algorithm. Finally, the actual motor model and complex function are used to verify the performance of the optimization algorithm.