Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm

Abstract

The optimum design of a passive vehicle suspension system is an important task to enhance riding performance. The passive suspension system is the most reliable system and cheapest. In this work, the Harris Hawk Optimization (HHO) algorithm was used to optimize the design of a passive vehicle suspension system. HHO was chosen due to its efficient exploration which increases the diversity of the released solutions and the various exploitation schemes which enhance the best-explored solutions. A novel scaled multi-objective function is developed which combines different objectives such as road holding and ride comfort which wasn’t be included in the previous work. Two vehicle models are used in the test are quarter and half vehicle model. The performance of the optimized HHO passive suspension system is compared to that in the literature work such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Fire-Fly Optimization algorithm (FA) optimized systems in addition to the traditional passive suspension system. The experimental results show the superiority of HHO over other algorithms in the literature in terms of quality of solution and robustness for the optimum design of the suspension system’s parameters. The peak value of the body acceleration of the HHO optimized suspension model is decreased by about 16.5% than the original passive quarter vehicle model. The dynamic tire load of the HHO optimized suspension system was enhanced by more than 7%. Besides, the peak value of body displacement is decreased by more than 25% from 4.6 cm for the traditional passive suspension system to 3.5 cm for the optimized HHO system.