Optimization Design of Cable Bundling Combining Hopfield Neural Network and Genetic Algorithm

To achieve minimal electromagnetic radiation emission and crosstalk under constraints of quantity limitations and capacity restrictions of cable bundling, a novel approach is proposed by integrating the rapid local convergence capability of Hopfield neural networks (HNN) with the global search ability of genetic algorithms (GA) for cable bundling optimization. Weight coefficients of penalty functions are automatically regulated through real-time monitoring of constraint violations during optimization to improve optimization effectiveness. Compared with standalone GA and HNN algorithms, this hybrid optimization algorithm reduces total computation time by 40 %, decreases crosstalk by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \sim 6 ~\text{dB}$</tex>, suppresses radiation emission by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$17 \%-28 \%$</tex>, and fully satisfies all constraints, witch demonstrate its applicability and superiority. This approach provides a novel solution for large-scale, strictly constrained and multi-objective electromagnetic compatibility problems of electronics systems.