The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-a-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.