两步式约束粒子群优化算法及其在新能源汽车轻量化设计中的应用

Two-step Constrained Particle Swarm Optimization Algorithm and Its Application to Lightweight Design of New Energy Vehicles

针对工程优化中,由于资源的限制最优解通常处于约束边界附近,而现有的约束优化算法侧重于约束机制处理而很少在约束边界附近的搜索问题,本文中提出两步式约束粒子群优化算法:第一步采用基于惩罚函数的粒子群算法寻优,采用速度重置指针避免寻优陷入停滞;第二步采用子集约束边界缩减方程获取约束边界信息,使用序列二次规划进行边界局部搜索,最后对比两步的结果以较优值作为全局最优解。考虑侧面碰撞和顶压溃两种工况,采用提出的改进算法对某款燃料电池汽车车身结构进行轻量优化,在保证结构碰撞安全性的前提下,部分板件的轻量效果达10. 92%。

In view of the problem that during engineering optimization the optima generally are located at near constraint boundaries due to the limitation of resources,but current constrained optimization algorithms tend to focus on handling constraint mechanism and seldom to search near constraint boundaries,an algorithm of two-step constrained particle swarm optimization(PSO)algorithm is proposed in this paper.In the first step,PSO algorithm based on penalty function is adopted for optimization with a pointer for speed reset used to prevent optimization from falling into stagnation.In the second step,subset constraint boundary reduction equation is employed to acquire information of constraint boundaries,and sequential quadratic programming(SQP)is utilized to conduct local search on boundaries.Finally the results of two steps are compared and the better one is chosen as the global optimum.As an application,the modified algorithm proposed is used to perform body lightweight optimization on a fuel cell sedan under both side collision and roof crash conditions,resulting in a 10.92%mass reduction of some body panels taking part in optimization while ensuring the structural crashworthiness of the vehicle.