Ant Colony algorithm is a novel simulated evolutionary algorithm.
蚁群算法是一种新型的模拟仿生算法。
In this paper, we review ant colony algorithm and particle swarm optimization.
本文讨论了群集智能的两种算法，蚁群智能与微粒群智能。
Ant colony algorithm is a new heuristic optimization method and it has many merits.
蚁群算法是一种新型启发式优化算法，具有较多优点。
The problem of dynamic route selecting by using ant colony algorithm was discussed.
论述蚁群算法在动态路径选择问题上应用。
And especially discusses the optimization of distribution route with ant colony algorithm.
并重点讨论了蚁群算法对配送路线的优化。
Here we use the ant colony algorithm to solve the problem to find the shortest path to travel.
这里我们运用蚁群算法对问题进行求解找到最短的旅行路径。
This paper presented a vehicle navigation system simulation model based on ant colony algorithm.
本文提出一种基于蚁群算法的车辆导航系统模拟模型。
This study proposes a kind of automatic test data generation method based on ant colony algorithm.
提出了一种基于蚁群算法的测试数据自动生成方法。
Also this paper discusses the basic method of ant colony algorithm to optimize PID control parameter.
探讨了遗传算法优化PID参数的基本方法，着重研究了改进遗传算法的PID控制优化。
Ant colony algorithm gains a wide attention for its branch calculation and fast restraining abilities.
蚁群算法因其的分布式计算、快速收敛性能受到广泛的关注。
The ant colony algorithm is a novel simulated evolutionary algorithm which shows many good properties.
蚁群算法是一种模拟进化算法，该算法具有许多优越的性质。
An adaptive ant colony algorithm is presented for the optimization of multi-minimum continuous function.
针对多极值连续函数优化问题，提出了一种自适应蚁群算法。
Proposing one concentrator open up topology design on the basis of ant colony algorithm of wired ac-cess network.
提出了一种基于蚁群算法的有线接入网络中集中器的拓朴设计。
An equal division point ant colony algorithm (EDPACA) was proposed to solve the swarm-robot mission planning problem.
提出了一种求解群集机器人协作任务规划问题的均分点蚁群算法(EDPACA)。
This paper presents a new fast ant heuristic for the QAP, the approximate-backbone guided fast ant colony algorithm (ABFANT).
针对QAP问题，提出了一种新的蚁群算法——近似骨架导向的快速蚁群算法（ABFANT）。
An improved ant colony algorithm is presented to seek out the most possible collapse path after the collapse path is defined.
而且定义了系统最大崩溃路径，并且提出了一种求解系统最大崩溃路径的蚁群算法。
Then, the thesis USES ant colony algorithm which is based on the elicitation of ant's feeding to solve the clustering problem.
然后成功地将聚类问题转换成蚁群求解问题，并使用基于蚂蚁觅食启发的蚁群算法进行聚类分析。
Then, the recent applications of the ant colony algorithm in discrete optimization, data mining and other field are summarized.
然后总结了近年来蚁群算法在组合优化、数据挖掘等领域的应用进展；
In response to this phenomenon, this paper describes the principle of ant colony algorithm and algorithm implementation process.
针对这个现象，本文首先介绍了蚁群算法的原理及算法实现过程。
Ant colony algorithm is a brand-new type of simulative evolution algorithm, which focus on its solution to conform optimized question.
蚁群算法是一种新型的模拟进化算法，重点始于组合优化问题的求解。
The paper proposed an algorithm based on ant colony algorithm for mining classification rule from the Student Scores Management Database.
本文采用一种基于蚁群算法的分类规则挖掘算法，其特征实质上是一种序列覆盖算法。
Power system restoration; Over-voltage prediction; Node importance; Ant Colony algorithm; Skeleton - network reconfiguration; Load recovery.
电力系统恢复；过电压预测；节点重要性；蚁群算法；网架重构；负荷恢复。
The ant colony algorithm has good adaptability when solving combined optimization problems, it isn t very good for continuous optimization problems.
蚁群算法在解决组合优化问题上有着良好的适应性，但直接应用于求解连续优化问题难以获得理想的效果。
It adopts 2-interchange mutation operator in ant colony algorithm to increase its ability of local search and to improve the quality of the solution.
该算法通过在蚁群算法中引入遗传算法的2 -交换变异算子，增强了算法的局部搜索能力，提高了解的质量。
It adopts 2-interchange mutation operator in ant colony algorithm to increase its ability of local search and to improve the quality of the solution.
该算法通过在蚁群算法中引入遗传算法的2 -交换变异算子，增强了算法的局部搜索能力，提高了解的质量。
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