作　　者： ; ; ;

机构地区： 同济大学电子与信息工程学院

出　　处： 《同济大学学报（自然科学版）》 2010年第1期76-80,共5页

摘　　要： 将铁路车流径路的优化问题分解为两个:车流排列离散空间中车流排列优化,车流排列的评价计算.在给出车流排列的评价函数的定义后,引入旅行商问题的描述,把车流排列优化问题归约为TSP问题,从而给出了车流排列优化的复杂性分析.引入优先权编码,定义种群个体的适应值函数和相应的遗传操作,给出相应的遗传优化算法,并以实际运营数据为依据,进行仿真计算.通过同禁忌搜索法计算结果比较,遗传算法虽然在解的精度上略逊一筹,但计算工作量小得多,硬件要求也没有禁忌搜索法高.因此,具体选用应以具体情况而定.条件允许,最好将两种算法结合起来使用. Car flow routing optimization is decomposed into two parts： the evaluative function computing of car flow order and the car flow order permutation optimization. The paper first presents the definition of the function to evaluate the order, and then the introduction of the traveling salesman problem. By inducing that the car flow order permutation optimization is traveling salesman problem, an analysis is made of the computational complexity of the car flow routing optimization. The paper also presents a genetic algorithm with designing of priority weight coding, fitness function of population individuals and relevant genetic operation. And simulation results based on a real case are given. Compared with tabu search algorithm, genetic algorithm is lower in computational precision while retaining such advantages of low computing cost and low request of hardware. Therefore, the chioce of the method is based on the real situation, and a combination of the two methods is proposed if permission.

关 键 词： 车流径路 遗传算法 评价函数 车流排列优化 旅行商问题 优先权编码

领　　域： [交通运输工程] [交通运输工程] [自动化与计算机技术] [自动化与计算机技术]