In order to solve the qos constrained multicast routing in mpls network , including bandwidth , delay , delay jitter , loss rate and cost , we construct a globally optimizing ant algorithm . based on the ant algorithm and qos , we study and develop an algorithm which can repair the multicast routing tree when the network multicast links fail . this algorithm can make traffic rerouting , recover network from failure and improve the ability of resilience 本文探讨了在mpls网络中，使用蚂蚁算法解决了包含带宽、延时、延时抖动、包丢失率和最小网络费用等约束条件在内的服务质量组播路由问题；并且在考虑服务质量的基础上，研究并设计了在网络出现链路故障之后，对组播路由树进行补救的算法，使得网络的业务流可以重路由，网络故障得到恢复，提高了网络的恢复力。
The research analyzed the main reasons for the premature , stagnation and slow constringency of standard ant algorithm , and proposed an improved version of the ant algorithm grounded on the original algorithm , which could effectively improve the rate of the algorithm convergence , and its application in tsp has achieved very good result 摘要分析了标准蚁群算法易于出现早熟、停滞和算法收敛速度慢的主要原因，在原有算法基础上提出了一种改进型的蚁群算法，该算法可以有效的提高收敛速度，并把该算法应用到tsp问题中，取得了很好的效果。
There is extensive application in each trade in non - linear science and technology , such as cluster analysis , genetic al gorithm , ant algorithm of algorithm of artificial intelligence . this text is utilized game theory and artificial intelligent algorithm to simulate quantitatively the whole course of formation and developping of regional economy for the first time , offer the brand - new train of thought and method for research of the field of regional economy 人工智能算法理论的聚类分析、遗传算法、蚂蚁算法等非线性科学技术在各个行业有着广泛应用，本文首次利用博弈理论和人工智能算法定量地模拟区域经济形成和发展的整个过程，为区域经济领域的研究提供了全新的思路和方法。
The algorithm is based on the breadth - first search and optimized by ant algorithm . ants system is an artificial system based on the behavior of real ant colonies , which is often used to solve combinatorial problems , such as traveling salesman problem ( tsp ) . in ant colony system ( acs ) , a set of cooperating agents called ants cooperate to find good solutions to tsp 本算法基于宽度优先并用蚂蚁算法优化，蚂蚁算法是一种基于真实蚁群的人工智能系统，常用于解决组合优化问题，如旅行商问题( tsp ) ，在蚂蚁算法中一组蚂蚁团结合作，寻找tsp问题的最优解。
Test vector generation based on ant algorithm is presented and implemented , the pheromone computation formula for sequential circuits and status transfer rules are given , and the test results are compared with the results of the other existing test generators - hitec , gatest , cris , digate and strategate , based on standard sequential circuits iscas ' 89 and other synchronous sequential circuits 提出并实现了基于蚂蚁算法的测试矢量生成，给出了针对时序电路测试矢量生成的信息素计算公式和状态转移规则。在iscas ’ 89标准时序电路和几个同步时序电路上实现了测试生成，并将生成结果和其它现有测试生成器( hitec ， gatest ， cris ， digate ， strategate )的生成结果作了比较、分析。
The first part mainly understands the knowledge of grid infrastructure and introduces the study of grid system in existence , applied globus based on linux7 . 2 system . the second part mainly introduces the study of task scheduling on grid . base on ant algorithm , we design task scheduler simulation with java which give a good method for task scheduling on grid 本文由两部分组成：第一部分侧重对现有网格系统的研究，在linux7 . 2环境中建立网格环境globus ，了解网格架构，部署网格服务；第二部分侧重对网格环境中任务调度的研究，根据改进的蚂蚁算法，用java设计分布式任务调度仿真器，为网格环境提供了一种新型的任务调度方法。
Set up geographical data base in order to store the area ' s geographical information the geographical data base can reduce the time of making the logistics plan greatly . on the basis of geographical data base , delivery goods directly can find the routing of providing and deliver in the geographical data base directly , for delivers goods for the demand most with the vehicle at least and delivers goods with time restriction problems , optimize the routing with a kind of algorithms of intelligence - ant algorithms 在车辆调度问题中的配送线路的生成问题上，首先分析了物流配送中运输线路的问题并建立了该问题的数学模型，发现合车送货可大大降低运输的成本。提出建立地理数据库用以存储配送区域的地理信息，大大减少生成配送计划的时间，并为运输线路的优化奠定基础。在地理数据库的基础上，单车配送可直接在地理数据库中查出配送线路，对于合车送货和有时间窗的配送线路生成问题采用一种启发式算法?蚂蚁算法进行优化。
Then ant algorithm to resolve tsp is adopted to carry out scheme optimization according to the positive feedback searching mechanism of ant colony system , and some improvements are made incorporating crossover and mutation operators of genetic algorithm , to obtain the optimal scheme rapidly and effectively 利用蚁群系统内在的正反馈寻优机制，将求解旅行商问题的基本蚂蚁算法应用于方案求解的组合优化过程，结合遗传算法的交叉变异操作，提出一种基于改进蚂蚁算法的求解方法，从而快速有效地获得了最优方案解。
