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。
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。
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。
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。
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。
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。
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