得到了上收敛群中元素的一些性质，利用这些性质证明了收敛群中某些子群的一些极大性质。
By use of these properties, we prove a kind of maximal property of subgroups in convergence group.
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。
Ant colony algorithm gains a wide attention for its branch calculation and fast restraining abilities.
采用基于动态信息素更新策略的改进蚁群优化算法，在保证优化目标的基础上，迅速收敛并得到最优解，从而提高调度系统的可行性，增强系统稳定性。
Secondly, the algorithm based on the dynamic pheromone updating ensures the quick convergence and the optimal solution, thus improving the feasibility and the stability of the schedule system.
此时问题已转化为多个簇的单模态问题，在各个簇中再利用保收敛微粒群优化器获得每个簇的最优解。
So multimodal problem has turn into unimodal problem in some clusters in which the optimal solution is guaranteedly located through the guaranteed convergence particle swarm optimizer.
然而，基本蚁群算法在求解组合优化问题过程中容易出现过早收敛或停滞现象。
However, the traditional algorithms have the problems of early convergence or stagnation in the process of combinatorial optimization problems.
与拟牛顿法和蚁群算法相比，新算法不仅提高了解的精确性，而且增强了收敛的可靠性。
Compared with the quasi Newton methods and ACA, the solution accuracy of new algorithm is not only improved, but also the convergent reliability is increased.
这大大增强了蚂蚁间的合作性，加快了蚁群算法的收敛速度，提高了全局搜索能力。
This can greatly strengthen the cooperation between ants, accelerate the convergence speed and enhance the global searching ability.
仿真实验表明：用蚁群算法训练神经网络，可兼有神经网络广泛映射能力和蚁群算法快速全局收敛的性能。
Simulation results show that extensive mapping ability of neural network and rapid global convergence of ant system can be obtained by combining ant system and neural network.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对K均值聚类算法依赖于初始值的选择，且容易收敛于局部极值的缺点，提出一种基于粒群优化的K均值算法。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
讨论一类可数离散半群上概率测度卷积幂的弱收敛性，主要结果是利用局部群化的观点给出了概率测度卷积幂弱收敛的一个充分条件。
The main result is that we get a sufficient condition for the weak convergence of convolution powers of probability measures, by using the method of local grouplization.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
算法中的信息素踪迹更新过程作为蚁群间的间接通信机制，将引导整个蚁群收敛到问题的优化解。
The pheromone trail updating procedure ACTS as an indirect communication mechanism within the ant colony, leading all the ants to converge to good Tours.
与标准微粒群算法相比，算法的全局搜索能力和收敛速度都得到了显著提高，同时能够有效避免早熟收敛。
Compared to standard PSO, its global searching ability and the speed of convergence is significantly improved, and the premature convergence problem is effectively avoided.
文中研究定义在紧有限交换半群上的概率测度，同时研究它们的合成收敛序列的性质。
In this paper, the probability measure defined on the compact finite commutative semigroup and us composition convergence is discussed.
发现基本蚁群优化算法存在慢收敛且易停滞等问题。
The ant colony optimization algorithm has found slow convergence and easy to stagnation.
但是作为一种新的群智能算法，人工鱼群算法有自身的不足，如算法的复杂度高、算法后期的收敛速度慢和收敛精度低等。
As a swarm intelligence algorithm, AFSA has its weakness, such as high complexity, low optimizing precision and low convergence speed in the later period of the optimization.
蚁群算法相对其它算法的主要优势是收敛速度明显快、计算精度高。
The advantage of ant colony algorithm over the other algorithm mainly lies in its obvious and quick convergence rate, its high accuracy.
它大大减少了蚁群算法的搜索时间，有效改善了蚁群算法易于过早地收敛于非最优解的缺陷。
It greatly reduces the searching time of ant colony algorithm. It also effectively ameliorates the disadvantage of easily falling in local best of ant colony algorithm.
分别用递推最小二乘算法、基本蚁群算法与混合蚁群算法训练模糊系统，混合蚁群算法的收敛效果优于递推最小二乘算法与基本蚁群算法。
The convergence error of hybrid ant colony algorithm is small in comparison to that of recursive least square algorithm and basic ant colony algorithm.
其次利用混合蚁群算法快速收敛和分布式求解的特点实现任务分配的组合优化。
Then applied a hybrid ant colony algorithm to accomplish combinatorial optimization of task allocation.
其次利用混合蚁群算法快速收敛和分布式求解的特点实现任务分配的组合优化。
Then applied a hybrid ant colony algorithm to accomplish combinatorial optimization of task allocation.
应用推荐