Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment

Although pure electric vehicles have the advantages of environmental protection and energy conservation, due to the constraints of power lithium-ion battery technology, they have a relatively short driving range and face problems such as high battery costs and low charging efficiency. Automotive electrical equipment affects the performance and safety of automobiles. In order to improve the efficiency and reliability of automotive electrical equipment, structural optimization is necessary. This article aims to improve the search ability and convergence speed of the algorithm by optimizing its parameters and introducing new genetic operations, in order to obtain a more optimal structure of automotive electrical equipment. Firstly, the basic principle and process of multi-population genetic algorithm were studied, and its problems in optimizing the structure of automotive electrical equipment were analyzed. Then, based on the characteristics of the problem, improvements were made to the multi-population genetic algorithm to improve its search ability. Through experimental verification, the improved multi-population genetic algorithm has achieved significant results in optimizing the structure of automotive electrical equipment. Compared with traditional algorithms, the improved algorithm has significantly improved its search ability and convergence speed. Through the optimized algorithm, a better structure of automotive electrical equipment was obtained, with higher efficiency and reliability.