目前，群智能理论研究领域有两种主要的算法：微粒群优化算法和蚁群优化算法。
The basic and typical algorithm of swarm intelligence is particle swarm optimization and Ant colony oprimation.
将该图书仓库布局模型分别基于装箱问题结构和广义指派问题结构，设计了两种不同的蚁群优化算法。
Two different Ant Colony Optimization heuristics based separately on Bin-packing Problem structure and Generalized Assignment Problem structure were designed for Books Warehouse Layout Problem.
为了构建一个合理的电网网架，本文对现有的网架优化方法进行了研究，重点分析了逐步倒推法和蚁群算法。
To build a rational grid network, this paper analyzed several grid framework optimization methods especially the ant colony algorithm and regradation step-by-step algorithm.
借鉴蚁群优化算法和粒子群优化算法的思想，提出了一种用于求解约束优化问题的连续域蚁群算法。
Using the idea of both ant colony optimization algorithm and particle swam optimization algorithm, a continuous domains ant colony algorithm for solving constrained optimization problem was proposed.
为求解该模型，并综合考虑优化质量和通信开销，采用了基于粗粒度模型的并行蚁群算法。
Considering the communication cost and optimization qualities, an ant colony algorithm based on the coarse-grain model is designed to solve the problem of this model.
本文在现代物流技术基础，特别是车辆调度和蚁群算法的基础理论指导下，针对现代物流运输车辆的调度优化问题，进行了理论、方法与模型的研究工作。
Based on the modern logistic technology, especially the vehicle scheduling and the ant algorithm theories, this paper make a research in theories, methods and models aiming at the present problems.
蚁群算法作为一种新型的优化方法，具有很强的适应性和鲁棒性。
Ant colony algorithms are robust and adaptable as novel optimization methods.
作为计算智能和群智能的重要分支之一，蚁群优化算法已经成功地应用于许多组合优化问题的求解。
As an important branch of computational intelligence and swarm intelligence, ACO have been successfully applied to solve many combinational optimization problems.
最后将粒子群算法和禁忌搜索算法思想引入到蚁群算法中，用于求解拉闸限电序位优化问题。
The particle swarm optimization algorithm and the Ant Colony Optimization are employed to develop a hybrid stochastic searching 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.
为了解决多重模态最优化问题，我们运用了一维离散优化方法、遗传算法和蚁群算法。
To solve the multimodal optimization problem the 1d discrete optimization methods, the genetic and Ant Colony algorithms are applied.
为了解决多重模态最优化问题，我们运用了一维离散优化方法、遗传算法和蚁群算法。
To solve the multimodal optimization problem the 1d discrete optimization methods, the genetic and Ant Colony algorithms are applied.
应用推荐