蚁群系统能够通过自适应调整不断优化算法的性能。
Ant Colony System(ACS) can develop excellent performance via self-adaptive behavior.
提出了一种蚁群系统与多选择背包问题融合的算法。
A hybrid algorithm combining ant colony system with multi-choice Knapsack problem was proposed.
介绍了用蚁群系统来解决方案设计中存在的组合优化问题。
A new technique based on ants system is put forward in this paper, which can solve the combinational optimization problem in project design.
同时，将遗传算法中排序的概念扩展到精英机制当中，形成基于优化排序的精英蚁群系统。
An excellence mechanism of the ant colony system is also formed based on the optimize compositor.
将量子群进化算法(QEA)与蚁群系统(acs)进行融合，提出一种新的量子蚁群算法(QACA)。
The algorithm is based on the combination of quantum evolutionary algorithm (QEA) and ant colony system (ACS), a new algorithm, quantum ant colony algorithm (QACA) is proposed.
针对典型的旅行商问题(TSP)进行对比实验，验证了所提出的算法在速度和精度方面优于传统的蚁群系统。
The contrasting experiments on the typical traveling salesman problem (TSP) show that the proposed algorithm is better than standard ant colony system in speed and accuracy.
为得到最优计划方案，针对搭接网络计划，建立了资源均衡优化的数学模型，并设计了自适应蚁群系统的求解算法与实现过程。
To get the optimum solution for the planning, I construct a model of resource optimization for spliced network planning, and design Adaptive Ant Colony Algorithm to solve it.
为得到最优计划方案，针对搭接网络计划，建立了资源均衡优化的数学模型，并设计了自适应蚁群系统的求解算法与实现过程。
To get the optimum solution for the planning, I construct a model of resource optimization for spliced network planning, and design Adaptive Ant Colony Algorithm to solve it.
应用推荐