Parameter Optimization of the Power and Energy System of Unmanned Electric Drive Chassis Based on Improved Genetic Algorithms of the KOHONEN Network

For unmanned electric drive chassis parameter optimization problems, an model containing power systems and energy was built using CRUISE, as the traditional genetic algorithm is prone to falling into local optima, improved isolation niche based on KOHONEN network clustering (KIGA) proposed. The simulation results show that proposed KIGA can reasonably divide initial populations. Compared with (GA) (IGA), achieve faster convergence a better global search ability. comprehensive performance of in terms economy increased by 8.26% set solutions. simultaneous system enhance efficient method for optimizing parameters chassis.