Time dependent vehicle routing problem with a multi ant colony system

The Time Dependent Vehicle Routing Problem (TDVRP) consists in optimally routing a fleet of vehicles of fixed capacity when travel times are time dependent, in the sense that the time employed to traverse each given arc, depends on the time of the day the travel starts from its originating node. The optimization method consists in finding solutions that minimize two hierarchical objectives: the number of tours and the total travel time.     

Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation

Hub and spoke type networks are often designed to solve problems that require the transfer of large quantities of commodities. This can be an extremely difficult problem to solve for constructive approaches such as ant colony optimisation due to the multiple optimisation components and the fact that the quadratic nature of the objective function makes it difficult to determine the effect of adding a particular solution component. Additionally, the amount of traffic that can be routed through each hub is constrained and the number of hubs is not known a-priori. Four variations of the ant colony optimisation meta-heuristic that explore different construction modelling choices are developed. The effects of solution component assignment order and the form of local search heuristics are also investigated. The results reveal that each of the approaches can return optimal solution costs in a reasonable amount of computational time. This may be largely attributed to the combination of integer linear preprocessing, powerful multiple neighbourhood local search heuristic and the good starting solutions provided by the ant colonies.     

基于蚁群优化最小二乘支持向量机模型的边坡稳定性分析

液固混合介质隔振器基于一种全新的工作机理,具有优良的隔振系统动力学特性. 混合介质由一类几乎不可压缩液体和许多可压缩的固体单元混合而成. 当振动、冲击发生时, 液体将动压力瞬间传递到所有单元体上, 使它们同时参与变形,从而有效隔离振动,大幅度吸收、损耗冲击能量; 若设计得当,这类隔振器可同时具有卓越的隔振和缓冲性能. 以空心橡胶球作为固体单元体, 分析了该单元体在有限变形情况下的变形规律, 分析了隔振器的非线性刚度特性; 采用MTS液压伺服试验系统进行了测试验证, 理论分析和试验结果具有较好的一致性. 建立了系统的非线性动力学方程, 采用多尺度摄动法获得了系统的频响特性, 发现系统具有软弹簧非线性动力学特性, 并在试验中得到了证实; 因为弹性恢复力中存在位移平方项, 通过试验和数值仿真进一步验证了系统响应的非对称性.     

Dispersive solid phase extraction combined with dispersive liquid–liquid extraction for the determination of BTEX in soil samples: ant colony optimization–artificial neural network

In this study, dispersive solid phase extraction combined with dispersive liquid–liquid extraction has been developed for the extraction of benzene, toluene, ethylbenzene, and xylenes isomers (BTEX) in soil samples prior to gas chromatography–mass spectrometry. The BTEX were extracted from soil sample into acetonitrile by dispersive solid phase extraction method, and the extract was then used as dispersive solvent in dispersive liquid–liquid extraction procedure. Ant colony optimization–artificial neural network (ACO–ANN) has been employed to develop the model for simulation and optimization of this method. The volume of dispersive solvent, volume of extraction solvent, extraction time, and ultrasonic time were the input variables, while the multiple response function (R_m) of analytes was the output. The optimum operating condition was then determined by ant colony optimization method. At the optimum conditions, the limit of detections of 0.12–0.75 ng g⁻¹ was obtained for the BTEX. The developed procedure was then applied to the extraction and determination of BTEX in the soil samples and one certified soil. Copyright © 2015 John Wiley & Sons, Ltd.     

基于蚁群优化的IP over WDM光网络动态生存性映射算法

随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战.本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点,借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略.与其他同类算法相比,该算法不再存储网络备用路由集和物理拓扑自身庞大的割集关系,在保证网络生存性效果的前提下,通过割集松弛条件有效降低了计算的时间复杂度,提高算法收敛速度,适应网络实时性要求.仿真结果表明:与传统最短路算法比较,该算法能有效提高动态光网络的可生存性映射性能和网络的资源利用效率,并降低业务阻塞率.     

一种最大最小蚂蚁系统的改进算法

最大最小蚂蚁系统是解决组合优化问题的有效算法.本文研究了最大最小蚂蚁系统的状态转移规则和信息素的更新方式,并提出了相应的改进策略,使得算法具有更高的收敛速度和更强的全局搜索能力.对旅行商问题的仿真结果表明改进的算法是可行有效的.     

A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks

A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.