Multi-Objective Optimization of Automotive Power Train based on Adaptive Genetic Algorithm for Maintaining Diversity

In order to solve the challenge of multi-objective optimization of automotive power train, in view of the shortcomings of the existing improved particle swarm optimization algorithm, an innovative multi-objective optimization method for power train based on adaptive diversity-preserving genetic algorithm is proposed. This new scheme uses the principle of overlapping sub-problem theory to accurately identify and locate the key influencing factors, and accordingly carries out a wise classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of adaptive genetic algorithm to maintain diversity, this scheme cleverly constructs a multi-objective design strategy for the drive train. The empirical results show that the proposed scheme shows a significant improvement compared with the traditional improved particle swarm optimization in terms of the accuracy of multi-objective optimization of the power train and the processing efficiency of key factors, showing its obvious strong advantages. In automobiles, power train multi-objective optimization plays a crucial role, which can accurately predict and optimize the growth trend and output results of automotive power train multi-objective optimization. However, in the face of complex simulation tasks, traditional improved particle swarm algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of multi-objective optimization of power train optimized by adaptive genetic algorithm to maintain diversity, and accurately controls the influencing parameters through the theory of overlapping sub-problems, and uses this as the road map for index allocation, and then uses the adaptive genetic algorithm to maintain diversity to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the multi-objective optimization of the automotive power train, the simulation scheme based on the adaptive genetic algorithm for maintaining diversity successfully overcomes the shortcomings of the traditional improved particle swarm optimization and significantly improves the accuracy and operation efficiency of the simulation.