Chang-sheng Zhu, Hongmei Huang, Yuan Yuan, Qing-rong Wang
{"title":"The research in public transit scheduling based on the improved genetic simulated annealing algorithm","authors":"Chang-sheng Zhu, Hongmei Huang, Yuan Yuan, Qing-rong Wang","doi":"10.1109/CINC.2010.5643737","DOIUrl":null,"url":null,"abstract":"In this work,we set up public transit planning model by analysising of vehicle dispatching and taking both interest of bus company and passenger into consideration. using the improved genetic simulated annealing algorithm(the improved GA-SA) to carry out optimization for public transit dispatching model,and overcomes the problems such as evolution is slow,precocious, local optimal solution and so on, it can find the approximate optimum solution, reliably, from the huge search space of scheduling optimization problem. intelligent scheduling optimization problem in the great search space to find reliable optimal solution or approximate optimal solution. Finally,we use MATLAB to carry on simulation experiment. the results show that the improved GA-SA has higher efficiency than traditional GA.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643737","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this work,we set up public transit planning model by analysising of vehicle dispatching and taking both interest of bus company and passenger into consideration. using the improved genetic simulated annealing algorithm(the improved GA-SA) to carry out optimization for public transit dispatching model,and overcomes the problems such as evolution is slow,precocious, local optimal solution and so on, it can find the approximate optimum solution, reliably, from the huge search space of scheduling optimization problem. intelligent scheduling optimization problem in the great search space to find reliable optimal solution or approximate optimal solution. Finally,we use MATLAB to carry on simulation experiment. the results show that the improved GA-SA has higher efficiency than traditional GA.