Pheromone Disruption of Argentine Ant Trail Integrity

Disruption of Argentine ant trail following and reduced ability to forage (measured by bait location success) was achieved after presentation of an oversupply of trail pheromone, (Z)-9-hexadecenal. Experiments tested single pheromone point sources and dispersion of a formulation in small field plots. Ant walking behavior was recorded and digitized by using video tracking, before and after presentation of trail pheromone. Ants showed changes in three parameters within seconds of treatment: (1) Ants on trails normally showed a unimodal frequency distribution of walking track angles, but this pattern disappeared after presentation of the trail pheromone; (2) ants showed initial high trail integrity on a range of untreated substrates from painted walls to wooden or concrete floors, but this was significantly reduced following presentation of a point source of pheromone; (3) the number of ants in the pheromone-treated area increased over time, as recruitment apparently exceeded departures. To test trail disruption in small outdoor plots, the trail pheromone was formulated with carnuba wax-coated quartz laboratory sand (1 g quartz sand/0.2 g wax/1 mg pheromone). The pheromone formulation, with a half-life of 30 h, was applied by rotary spreader at four rates (0, 2.5, 7.5, and 25 mg pheromone/m²) to 1- and 4-m² plots in Volcanoes National Park, Hawaii. Ant counts at bait cards in treated plots were significantly reduced compared to controls on the day of treatment, and there was a significant reduction in ant foraging for 2 days. These results show that trail pheromone disruption of Argentine ants is possible, but a much more durable formulation is needed before nest-level impacts can be expected. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

基于蚁群算法的区域覆盖卫星星座优化设计

首先建立了以区域覆盖性能为目标的卫星星座参数优化模型,通过在连续域定义新的蚂蚁分类与转移策略改进了蚁群算法,使算法能够兼顾多目标优化时目标不兼容的情况,给出了基于该算法实现星座参数优化的框架。优化仿真实验验证了该算法的有效性,可为星座方案决策提供有力的支持。     

词汇、语法和传情达意--一个湖边餐厅的习作

建筑设计往往是以传达情感为最终目的，因此语言的简单和熟练便显得愈发的重要。这个餐厅的设计尝试以简单的词汇组成直截的语言，力图清晰明确的传递建筑师的情感。     

改进蚁群算法在路径规划中的应用

针对传统蚁群算法在机器人路径规划时存在收敛速度慢、易陷入局部最优等问题,提出了一种基于自适应归档更新的蚁群算法。根据路径性能指标建立多目标性能评估模型,对最优路径进行多指标优化;采用路径方案归档更新策略进行路径方案的更新和筛选,提高算法的收敛速度;当搜索路径进入不可行区域时,采用自适应路径补偿策略转移不可行路径节点,构造可行路径,减少死锁蚂蚁数量;若算法无法避开障碍或者进入停滞状态,则进行种群重新初始化,增加物种多样性,避免算法陷入局部最优。仿真实验表明,改进后的算法收敛速度更快、收敛精度更高、稳定性更好。     

基于蚁群优化的WSN能耗均衡链状路由协议

为了从路由技术的角度解决无线传感器网络的能耗问题,综合蚁群优化算法和PEGASIS协议的思想提出了ACO-PEGASIS路由协议;该协议采用蚁群优化算法构建通信链,解决了PEGASIS协议中由于贪婪算法的局部性产生的相邻节点间的长链问题;并在成链过程中综合考虑节点间距离、节点剩余能量等因子,以均衡全网的能量消耗;同时根据距离和能量因素选取链头节点并采用一定措施降低链头重选次数;仿真结果表明,与PEGASIS协议相比,该协议更加有效地均衡了全网的能量消耗,延长了网络的生存周期。     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

机器人三维路径规划问题的一种改进蚁群算法

本文研究移动机器人三维空间路径规划问题,针对三维空间的复杂地形特点,提出了一种基于改进蚁群算法的路径规划算法。文中首先描述了一种简单有效的环境建模方法,然后给出了算法在信息素呈现、路径点选取、信息素更新以及启发式函数设计等方面的改进方法。仿真结果证明了算法的可行性和可靠性。     

改进的蚁群算法在飞行路径模型中的应用

为了保障飞机安全地运行,FAA规定每架飞机在飞行一定时间后都要进行周期性检修。通常考虑到检修成本,任一种检修都要在指定的机场完成,因此要求飞机每飞行一定的小时数后都要返回到指定的维护基地进行定期检修。如何合理地进行飞机飞行路径的调度以使飞机周期性地返回指定检修机场进行检修是本文研究的关键。为了问题简化,本文主要针对单检修基地、单机型的检修路径调度问题进行研究。本文结合经典蚁群算法,对现有算法参数的设置及信息的更新等方面进行改进后应用到飞行路径模型中,并运用Matlab对该算法编程,运用具体数据进行计算实现,确保了飞机周期性返回检修机场,实现周期性检修。     

基于模型的子空间聚类与时间段蚁群算法的合同生产批量调度方法

针对目前冷轧薄板厂生产流程复杂、大量的多品种小批量合同并线生产,导致难以制定生产计划的问题,本文提出了混合模型子空间聚类（Subspace clustering mixed model,SCMM）方法,以合同中待加工钢卷的宽度、冷轧机组的入口厚度、 出口厚度以及合同的交货期为约束,对待生产合同进行组批. 依据冷轧厂实际生产过程,将冷轧机组视为核心节点,考虑准时交货、 在制品库存和生产流向产能分配的要求,对组批后的生产合同建立全流程合同计划模型,并且利用提出的时间段蚁群算法（Time-section ant colony optimization,TSA）,制定合同计划.利用生产过程的实际数据测试,本文的方法优于人工排产,可以满足制定冷轧薄板全流程生产计划的要求.     

改进的蚁群算法在溶质运移参数识别问题中的应用

非饱和土壤水分和溶质运移参数(扩散率、导水率和水动力弥散系数)取值范围较大,往往跨越几个数量级。采用传统离散化蚁群算法求解此类问题,所需节点较多,这会造成算法收敛时间较长。该文在传统蚁群算法基础上,对蚂蚁搜索路径进行改进,改进后的蚁群算法寻优路径由参数精度位数(整数位和小数位)、参数个数以及0–9十个数字构成,并将路径解码公式修改为具有判别参数正负功能的解码公式。采用改进的连续蚁群算法对非饱和溶质运移参数识别优化模型进行求解。数值模拟表明相同迭代次数下改进的蚁群算法比传统蚁群算法耗时少,算法计算时间与迭代次数满足线性关系,含水率和溶质浓度实测值与计算值吻合较好、相关性较高。