Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating mod...Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.展开更多
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.展开更多
Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Thi...Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Things platform and the merging vehicle operation adjustment into the Flow-Shop scheduling problem in manufacturing systems,this paper has constructed the optimization model with a two-lane vehicle operation adjustment. With respect to the large model solution space and complex constraints,a better solution algorithm is proposed based on ant colony algorithm for optimal quick solution. The simulation results show that the algorithm is feasible and the approximate optimal solution can be quickly obtained.展开更多
山区输油管道在突发泄漏时,由于地形复杂、道路稀疏及环境敏感度高等原因,抢险工作往往面临严峻挑战。针对此问题提出了一种改进蚁群算法(Improved Ant Colony Optimization,I-ACO),通过构建融合地形阻力、泄漏风险与环境敏感度的综合...山区输油管道在突发泄漏时,由于地形复杂、道路稀疏及环境敏感度高等原因,抢险工作往往面临严峻挑战。针对此问题提出了一种改进蚁群算法(Improved Ant Colony Optimization,I-ACO),通过构建融合地形阻力、泄漏风险与环境敏感度的综合成本函数,将多维约束定量化,并引入动态信息素挥发率、已有道路先验偏好及局部搜索策略,以提高算法收敛速度并减少陷入局部最优的可能。仿真结果表明,在20×20网格测试中,I-ACO在已有道路与环境敏感区等多场景下的收敛速度比传统蚁群算法最高提升60.0%,敏感区穿越减少57.1%;在200×200真实地形映射中,收敛速度提升17.5%,路径更倾向于选取平缓及风险较低区域,更符合应急抢险需求。该研究可为山区输油管道泄漏抢险提供高效、可靠的路径优化方法。展开更多
基金Sponsored by the National High Technology Research and Development Program of China(2006AA701306)the National Innovation Foundation of Enterprises(05C26212200378)
文摘Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.
基金supported by the National Natural Science Foundation of China(60573159)
文摘Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
基金Sponsored by the Natural Science Foundation of Shandong Province(Grant No.ZR2011FL006)2012 International Cooperation Training Fund of Outstanding Young Backbone Teachers of Colleges and Universities in Shandong Province,and Shandong Province Science,2012 Shandong ProvinceSpark Program and Technology Development Plan(Grant No.2011YD01044)
文摘Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Things platform and the merging vehicle operation adjustment into the Flow-Shop scheduling problem in manufacturing systems,this paper has constructed the optimization model with a two-lane vehicle operation adjustment. With respect to the large model solution space and complex constraints,a better solution algorithm is proposed based on ant colony algorithm for optimal quick solution. The simulation results show that the algorithm is feasible and the approximate optimal solution can be quickly obtained.
文摘山区输油管道在突发泄漏时,由于地形复杂、道路稀疏及环境敏感度高等原因,抢险工作往往面临严峻挑战。针对此问题提出了一种改进蚁群算法(Improved Ant Colony Optimization,I-ACO),通过构建融合地形阻力、泄漏风险与环境敏感度的综合成本函数,将多维约束定量化,并引入动态信息素挥发率、已有道路先验偏好及局部搜索策略,以提高算法收敛速度并减少陷入局部最优的可能。仿真结果表明,在20×20网格测试中,I-ACO在已有道路与环境敏感区等多场景下的收敛速度比传统蚁群算法最高提升60.0%,敏感区穿越减少57.1%;在200×200真实地形映射中,收敛速度提升17.5%,路径更倾向于选取平缓及风险较低区域,更符合应急抢险需求。该研究可为山区输油管道泄漏抢险提供高效、可靠的路径优化方法。
