Hybrid GA for multi-objective design of heavy goods vehicle suspension system

The multi-objective design of suspension system for multi-axle heavy goods vehicle (HGV) is presented in this paper. Minimum road damage and goods damage are taken as design criteria for design. The objective function and constraint calculation in suspension design involve dynamic responses of the vehicle that has a high computational cost. Hence, a hybrid genetic algorithm (GA) is proposed for fast convergence. Proposed hybrid GA is designed using the selection method of two different genetic algorithms, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and 2-objective geometry inspired genetic algorithm (2GIGA). Proposed hybrid GA is tested for different benchmarking optimization problems along with suspension optimization problems. Two types of suspension systems, passive and semi-active suspensions, are designed. A semi-active suspension system design involves designing a controller for a controllable damper along with passive elements. An improvised control law using a proportional integral derivative (PID) controller is used for the semi-active suspension system of multi-axle HGV. This control law is designed in such a way that it will eliminate the need for a passive damper in a semi-active suspension system. Results of the proposed hybrid GA are compared with the NSGA-II and 2GIGA. The proposed hybrid GA is performing better and saves significant computational cost for the suspension design of HGV.