Car side impact design optimization problem using giant trevally optimizer

Abstract

One of the evaluation criteria used to determine the effectiveness of newly developed algorithms is their application to solving real-world mechanical problems. Car side impact design optimization problem (CSDP) is one of the most complex problems in the industry field. According to international safety standards, a car is exposed to a side-impact. Here, the aim is to minimize the weight using eleven mixed design variables while maintaining safety performance according to the standards. In this paper, the recently developed Giant Trevally Optimizer (GTO) is used to be implemented on the aforementioned complex optimization problem. GTO mimics the hunting strategies of marine predator giant trevally while hunting birds. The effectiveness of GTO is tested by comparison with ten well-known optimizers by solving the car side impact problem. The results show that GTO generally outperforms the tested competitors and demonstrate the practicability of the GTO optimizer in solving challenging real-world problems. Moreover, new cost function has been found by GTO for CSDP.