摘要
考虑城市高校校区分散、教职工通勤安全、高校和教职工双方利益及道路路况影响校车行驶速度等因素,建立高校校车联营的协同车辆路径问题模型。充分利用蚁群优化算法和遗传算法的优势,引入了平滑机制和混沌搜索机制,构造了混合蚁群协同算法。对实例进行仿真表明,该算法在收敛速度和寻优结果两方面都优于遗传算法和蚁群优化算法,而且高校校车联营的模式不仅节约了高校经费开支,还缓解了交通拥堵,因此对城市的发展有重要的意义。
Considering these facts, such as scattered campuses of colleges and universities in a city, staff commuter security, common interest of both college and staff and road states can influence school bus speed etc. , establishing school bus routing problem for joint ventures strategy （SBRPJVS） model. Making full use of the advantages of ant colony optimization （ACO） and genetic algorithm （ GA）, this paper introduced the smooth mechanism and chaotic search, and constructed the hybrid col- laborative ant colony Optimization algorithm （HCACO）. Experiments show that the algorithm in convergence speed and optimal results are superior to both genetic algorithm and ant colony optimization algorithm. The school bus joint operation mode not only can save the expenditure for colleges and universities, but also easing traffic congestion, it has important significance to the development of the city.
出处
《计算机应用研究》
CSCD
北大核心
2015年第3期683-688,共6页
Application Research of Computers
基金
国家自然科学基金资助项目(61074147
61074185)
广东省自然科学基金资助项目(S2011010005059
8351009001000002)
广东省教育部产学研结合资助项目(2012B091000171
2011B090400460)
广东省科技计划资助项目(2012B050600028
2010B090301042)
关键词
校车路径问题
协同算法
蚁群优化算法
遗传算法
平滑机制
混沌搜索
school bus routing problem（SBRP）
collaborative algorithm
ant colony optimization
genetic algorithm
smoothmechanism
chaotic search
