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针对电力机车二系悬挂调簧分析数学模型算法的优化问题,提出一种遗传算法(GA)与蚂蚁算法(AA)相结合的混合优化算法。其基本思想是:首先采用遗传算法以较少的进化代数进行全局快速随机搜索,获得若干可能的(近似)优化解,以此生成蚂蚁算法初始信息素分布,再用后者求得全局优化精确解。对国产SS3B和SS9型机车的应用结果表明,对同一车体进行多次优化计算试验,混合优化算法的搜索寻优过程均能稳健一致地收敛到全局优化解,可明显缩短二系支承载荷调整调簧计算所需时间,使调簧试验的实时性大为提高。对于二系为高圆簧的SS9型机车,混合算法平均用时比迭代算法和单一遗传算法分别减少约74%和29%。
In order to solve the optimization problem of mathematic model of secondary mooring torsion spring for electric locomotive, a hybrid optimization algorithm combining Genetic Algorithm (GA) with Ant Algorithm (AA) is proposed. The basic idea is as follows: First, a global fast random search with fewer evolutionary algebra by genetic algorithm is used to obtain several possible (approximate) optimal solutions to generate the initial pheromone distribution of the ant algorithm, and then the global optimization accuracy solution. The application results of the domestic SS3B and SS9 locomotives show that the search optimization process of the hybrid optimization algorithm converges to the global optimal solution steadily and uniformly, which can significantly reduce the load adjustment of the secondary support Tune spring calculation of the time required, so that real-time Tune Spring test greatly improved. For SS9 locomotives with two-unit high-leaf springs, the hybrid algorithm reduced the average time by about 74% and 29%, respectively, compared with the iterative algorithm and the single genetic algorithm.