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Lightweight vehicle design has become an imperative in today's automotive industry. And it is a difficult task, which usually involves non-obvious decisions beyond the designer's intuition. In practice, optimization through finite element simulation is prohibitively inappropriate due to massive computational cost. As a consequence, approximation method is extensively used. In this paper, lightweight design of front side rail through high strength steel is performed. And the advantages of weighted average surrogate (WAS) for approximating the crashworthiness responses in frontal crash are also discussed. It shows the strategy of using WAS is effective, with great potential applications for vehicle crashworthiness approximation and lightweight design.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.