A Cable Layout Optimization Method for Electronic Systems Based on Ensemble Learning and Improved Differential Evolution Algorithm
Xu Yang, Dejian Zhou, Wei Song, Yulai She, Xiaoyong Chen
Abstract
In this article, a method for solving cable layout optimization problems in electronic systems based on ensemble learning (EL) and improved differential evolution (DE) algorithm is proposed. Using this method, we produced two cable layout schemes with the considerations of the electromagnetic compatibility performance and the total length of cables. First, several different individual machine learning (ML) models for all output variables were established. On this basis, we established EL models with better performance than previous individual ML models. Then, aiming at the optimization problem presented in this article, an improved DE algorithm was proposed. On this basis, a method for solving cable layout optimization problems in electronic systems based on EL and the improved DE algorithm was proposed. Finally, the effectiveness of the proposed method was verified by two examples of electronic system cable layout optimization with different routing modes. Each example problem was solved using four different methods. The results indicate that the method proposed here outperformed existing methods.