交通系统智能决策的算法研究
|
摘要
遗传算法是近年来迅速发展起来的一种全新的随机搜索与优化算法,但由于其自身固有的缺陷,通常优化过程的收敛速度较慢,局部搜索能力不足,而且算法稳定性较差。而蚁群算法广泛应用在旅行商问题计算中,目前是较好的求解最短路由问题的算法之一。就其自身来说有很多优点,如正反馈性、鲁棒性和智能性,但是在寻优过程中容易陷入局部最佳的缺点。针对上述情况,将遗产算法与蚁群算法相结合,用于实际交通系统寻找最优路径的问题中,并定义了目标函数,以路径可靠性和路径长度为优化目标,寻找最合适的救灾路线。最后通过实际计算结果的对比验证,说明了该方法的有效性。
The optimal path analysis is some difficult because it costs too much time in order to find an optimum so- lution.In the paper,several algorithms such as ant colony algorithm(ACA) and genetic algorithm(GA) are em- ployed to study the real transportation system to find out the best line for distaster relief.The ACA and GA methods can solve some transporation system planning and other optimization problems,but they all have some defects.So, an improved GA is presented for the determination of the optimal path between any two points in transportation net- work.The calculated results show that the improved GA has better performance than GA and ACA alone.
The optimal path analysis is some difficult because it costs too much time in order to find an optimum so- lution.In the paper,several algorithms such as ant colony algorithm(ACA) and genetic algorithm(GA) are em- ployed to study the real transportation system to find out the best line for distaster relief.The ACA and GA methods can solve some transporation system planning and other optimization problems,but they all have some defects.So, an improved GA is presented for the determination of the optimal path between any two points in transportation net- work.The calculated results show that the improved GA has better performance than GA and ACA alone.
引文
[1]柳春光，林皋，李宏男，周晶．生命线地震工程导论[M]．大连：大连理工大学出版社，2005．
[2]Chu Xue song,Liu Chun guang,Li Liang.Determining the shortest path between two points using ant colony algorithm [C]//The 6~(th) International Symposium on Test and Measurement.Da Lian,2005:2925-2928.
[3]柳春光，焦双健，史文谱，刘殿魁．城市震后救灾系统救灾决策研究[J]．自然灾害学报，2000，9(3)：112-117．
[4]王为学，等．城市交通规划理论与应用[D]．南京：东南大学，1998．
[5]吴斌，史忠植．一种基于蚁群TSP问题分段求解算法[J]．计算机学报，2001，24(12)：1328-1333．
[6]王颖，谢剑英．一种基于蚁群系统的多电路由新算法[J]．计算机工程，2001，27(1)：55-56．
[7]Chang C S,Tian L,Wen F S.A new approach to fault section estimartion in power systems using ant system[J].Electric Power Systems Research, 1999,49(1):63-70.
[8]Wang Lei,Wu Qidi.Futher example study on ant system algorithm based continuous space optimization[C]//Proc.4~(th) WCICA 2002 IEEE USA, 2002:2541-2545.
[9]马良．一种全局优化的新方法[J]．系统工程与电子技术，2000，22(9)：61-63．
[10]林锦，朱文兴．凸整数规划问题的混合蚁群算法[J]．福州大学学报，自然科学版，1999，27(6)：5-9．
[11]孙艳丰，王众托．遗传算法在优化问题中的应用[M]．北京：国防工业出版社，1999．
[12]吴庆洪，张纪会，徐心和．具有变异特征的蚁群算法[J]．计算机研究与发展，1999，36(10)：1240-1245．
[2]Chu Xue song,Liu Chun guang,Li Liang.Determining the shortest path between two points using ant colony algorithm [C]//The 6~(th) International Symposium on Test and Measurement.Da Lian,2005:2925-2928.
[3]柳春光，焦双健，史文谱，刘殿魁．城市震后救灾系统救灾决策研究[J]．自然灾害学报，2000，9(3)：112-117．
[4]王为学，等．城市交通规划理论与应用[D]．南京：东南大学，1998．
[5]吴斌，史忠植．一种基于蚁群TSP问题分段求解算法[J]．计算机学报，2001，24(12)：1328-1333．
[6]王颖，谢剑英．一种基于蚁群系统的多电路由新算法[J]．计算机工程，2001，27(1)：55-56．
[7]Chang C S,Tian L,Wen F S.A new approach to fault section estimartion in power systems using ant system[J].Electric Power Systems Research, 1999,49(1):63-70.
[8]Wang Lei,Wu Qidi.Futher example study on ant system algorithm based continuous space optimization[C]//Proc.4~(th) WCICA 2002 IEEE USA, 2002:2541-2545.
[9]马良．一种全局优化的新方法[J]．系统工程与电子技术，2000，22(9)：61-63．
[10]林锦，朱文兴．凸整数规划问题的混合蚁群算法[J]．福州大学学报，自然科学版，1999，27(6)：5-9．
[11]孙艳丰，王众托．遗传算法在优化问题中的应用[M]．北京：国防工业出版社，1999．
[12]吴庆洪，张纪会，徐心和．具有变异特征的蚁群算法[J]．计算机研究与发展，1999，36(10)：1240-1245．
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心