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。
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。
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。
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。
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