Therefore, a quickly convergent version of the ACO algorithm is presented.
为此，提出了一种高速收敛算法。
A new ACO algorithm for unconstrained nonlinear integer optimization problem is present.
提出了一种新的蚁群算法来求解无约束的整数规划问题。
In order to improve the performance of ACO algorithm, an ACO algorithm with the dynamic parameter decision model was presented in this paper.
为使蚁群优化算法在应用中发挥最佳的寻优性能，提出带有动态参数决策模型的改进蚁群优化算法。
ACO algorithm is basically a multi agent system where low level interactions between single agents result in a complex behavior of the whole ant colony.
ACO算法本质上是一个多代理系统，在这个系统中单个代理之间的交互导致了整个蚁群的复杂行为。
Relative to the ACO algorithm, GPSO algorithm is better than the ACO algorithm whether in the search speed, search precision, or in the algorithm running time.
相对于蚁群算法来说，不论是在搜索速度、搜索精度，还是在算法的运行时间上，GPSO算法都要优于蚁群算法。
By analyzing the function and significance of parameters, the parameter setting of ACO algorithm can be described as a multifactor and multilevel optimization problem.
通过对蚁群优化算法各操作参数作用与意义的分析，将蚁群优化算法的参数设定描述为一个多因素多水平优化设计问题。
On the algorithm solving model, this paper introduces ACO algorithm, and makes a comparison between the ACO algorithm and other optimization algorithm, then points out its advantages.
对于其模型的求解算法，文章引入了蚁群算法，并将蚁群算法与其它优化算法做了比较，说明其优点所在。
But the algorithm easily trapped into local optimal solution and solved the problem more slowly, this paper constructed Max-Min ACO algorithm through the improvement and adjustment of ACO algorithm.
针对该算法易陷入局部最优解、求解速度较慢的缺陷，本文通过对蚁群算法的改进和调整，构造出最大—最小蚁群算法。
Ant Colony optimization (ACO) is a new-style simulating evolution algorithm. The behavior of real ant colonies foraging for food is simulated and used for solving optimization problems.
蚁群算法是一种新型的模拟进化算法，它通过模拟蚁群在觅食过程中寻找最短路径的方法来求解优化问题。
Global path planning problem for autonomous underwater vehicle (AUV) based on large-scale chart data is investigated by using ant colony optimization (in shorts, ACO) algorithm.
在大范围海洋环境中，应用蚁群算法对自主式水下潜器(AUV)的全局路径规划问题进行了研究。
Two improvements on Ant Colony Optimization(ACO) algorithm is presented in this paper.
蚁群算法在处理大规模优化问题时效率很低。
In this paper, ant colony algorithm (ACO) was applied to solve the prioritizing problem of flight landing.
本文将蚁群算法用于着陆航班的排序问题。
Such algorithms include evolutionary algorithm (EA), particle swarm optimization (PSO), artificial immune system (AIS) and ant colony optimization (ACO) and so on.
这类算法主要包括进化算法(EA)、粒子群算法(PSO)、人工免疫系统(ais)和蚁群算法(aco)等等。
Ant Colony Optimization (ACO) algorithm is a new swarm intelligence heuristic algorithm.
蚁群算法是一种新兴的群智能算法。
Then the principle, the model, the characteristics and the management about the basic algorithm of ACO are also presented.
介绍了蚁群算法基本模型的原理、特点和实现方法，并介绍了目前针对基本蚁群算法不足所提出的改进措施。
Experimental results show that the novel algorithm can get a better solution, and convergence rate is enhanced with a comparison to the I-ACO-B algorithm.
实验结果表明，与I-ACO-B算法相比，该算法不仅能获得更好的解，且收敛速度也有一定的提高。
Experimental results show that the novel algorithm can get a better solution, and convergence rate is enhanced with a comparison to the I-ACO-B algorithm.
实验结果表明，与I-ACO-B算法相比，该算法不仅能获得更好的解，且收敛速度也有一定的提高。
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