摘要
针对机车二系弹簧支承载荷均匀性分配调整的复杂多变量优化问题,提出了综合运用遗传算法(GA)和蚂蚁算法(AA)的混合优化调整算法模型。该算法模型首先采用GA进行全局快速随机搜索,获得若干候选的近似优化解,以此生成蚂蚁算法初始信息素分布,再用AA求得全局优化精确解。论文给出了混合算法模型的设计。对SS3b和SS9机车的仿真计算结果表明,该方法应用于二系调簧的多维连续性空间优化问题,可获求解性能和时间效率的综合提高。
A hybrid algorithm based on the combination of Genetic Algorithm（GA） and Ant Algorithm（AA） is introduced for a complicated multi-variable optimization problem of adjusting locomotive secondary spring loads to the minimized unbalance state. The algorithm firstly adopts GA, with its capability of fast and global stochastic searching,to find a number of approximate optimal solution candidates, by which the initial pheromone distribution for AA is generated,and then applies AA to obtain precise optimized solution to the problem. Detailed model of the hybrid algorithm is presented. Computing results of Simulation on SS3b and SS9 locomotives show that the algorithm achieves better performance in terms of more accurate and faster real-time computing ability in solving the continuous multi-dimension space optimization problem of the spring loads adjustment.
出处
《系统工程》
CSCD
北大核心
2005年第8期116-120,共5页
Systems Engineering
基金
铁道部科技基金资助项目(J2000Z040)
作者简介
潘迪夫（1957-），男，广东兴宁人，中南大学交通运输工程学院副教授，研究方向：载运工具性能优化智能测控技术及应用。
