Solution of the inverse problem in solidification of binary alloy by applying the ACO algorithm

The paper presents a solution of the inverse problem consisting in reconstruction of the heat flux and the distribution of temperature in the process of binary alloy solidification when the temperature measurements in the selected points of the alloy are known. The considered task is mathematically modelled by means of the heat conduction equation with the substitute thermal capacity and with the liquidus and solidus temperatures varying in dependence on the concentration of the alloy component, whereas for describing the concentration the lever arm model is applied. An important part of the procedure consists in minimization of some functional executed with the aid of ACO algorithm.     

An adaptive ant colony optimization algorithm for constructing cognitive diagnosis tests

A critical issue in the applications of cognitive diagnosis models (CDMs) is how to construct a feasible test that achieves the optimal statistical performance for a given purpose. As it is hard to mathematically formulate the statistical performance of a CDM test based on the items used, exact algorithms are inapplicable to the problem. Existing test construction heuristics, however, suffer from either limited applicability or slow convergence. In order to efficiently approximate the optimal CDM test for different construction purposes, this paper proposes a novel test construction method based on ant colony optimization (ACO-TC). This method guides the test construction procedure with pheromone that represents previous construction experience and heuristic information that combines different item discrimination indices. Each test constructed is evaluated through simulation to ensure convergence towards the actual optimum. To further improve the search efficiency, an adaptation strategy is developed, which adjusts the design of heuristic information automatically according to the problem instance and the search stage. The effectiveness and efficiency of the proposed method is validated through a series of experiments with different conditions. Results show that compared with traditional test construction methods of CDMs, the proposed ACO-TC method can find a test with better statistical performance at a faster speed.     

基于蚁群优化算法和k阶近邻法的暂态稳定评估特征选择

提出了基于蚁群优化算法和k阶近邻法相结合的嵌入式特征选择算法.选择稳态潮流量构成电力系统暂态稳定评估的输入特征集,针对输入特征集包含的大量冗余信息,特征选择结果中可能包含一定冗余特征的缺陷,先用聚类的方法裁剪冗余性特征,然后用所提算法选择和稳定状况强相关的关键特征,提高了特征选择的 效率.通过对3机9节点和10机...     

联合蚁群算法和PCNN的脑部MRI图像分割方法

采用蚁群算法(ACO)联合脉冲耦合神经网络(PCNN)的脑部磁共振成像(MRI)图像分割方法。其中利用ACO解决了PCNN参数设置困难的问题,同时能够克服图像的低对比度和噪声对图像分割的影响,实现图像的精确分割。首先利用ACO的全局搜索能力,以图像信息熵与灰度期望值的和作为ACO的目标函数,对PCNN的3个关键参数β、αE和VE进行设定;然后基于PCNN简化模型,结合最大熵值准则对脑部MRI图像进行分割;最后对分割结果进行面积滤波,得到最终的分割结果。实验结果表明,本文方法能够实现脑部MRI图像的自动分割,具有较高的精度和较强的鲁棒性。对于没有噪声的图像,本文方法分割结果的平均正确提取率达到97.0%以上,平均错误提取率达到0.4%以下,平均杰卡德相似系数达到94.8%以上;对于添加了不同级别噪声的图像,本文方法的分割效果也优于FCM和自适应PCNN。     

Particle filter with ant colony optimization for frequency offset estimation in OFDM systems with unknown noise distribution

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes inter-carrier interference (ICI). In this paper, a particle filter (PF) method augmented with ant colony optimization (ACO) is developed to estimate the CFO. The ACO for continuous domains is incorporated into PF to optimize the sampling process. Unlike the standard PF, resampling is not required in the method. Moreover, it does not require the noise distribution. Simulation results show that the proposed method is effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems.     

An Improved Continuous Ant Algorithm for Optimization of Water Resources Problems

Ant colony optimization was initially proposed for discrete search spaces while in continuous domains, discretization of the search space has been widely practiced. Attempts for direct extension of ant algorithms to continuous decision spaces are rapidly growing. This paper briefly reviews the central idea and mathematical representation of a recently proposed algorithm for continuous domains followed by further improvements in order to make the algorithm adaptive and more efficient in locating near optimal solutions. Performance of the proposed improved algorithm has been tested on few well-known benchmark problems as well as a real-world water resource optimization problem. The comparison of the results obtained by the present method with those of other ant-based algorithms emphasizes the robustness of the proposed algorithm in searching the continuous space more efficiently as locating the closest, among other ant methods, to the global optimal solution.     

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.     

基于蚁群算法的遥感影像传输资源调度方法

针对遥感影像数据量大,多用户并发请求造成服务器负载加重,使遥感影像传输效率逐渐降低的问题,提出一种在多线服务器环境下分块调度遥感影像资源的策略。该策略采用改进的蚁群优化(IACO)算法,通过引入一个线路等待因子γ动态选择当前最优的线路进行传输,从而提高传输效率。对IACO、ACO、Max-min、Min-min和Random算法进行了对比实验,IACO算法在客户端的任务完成时间和服务器端的执行时间与其他算法相比均是最少的,且随着任务数目的增加,效果更明显;同时IACO算法的线路资源的利用率也更高。仿真结果表明:多线服务器分块调度策略与改进蚁群算法相结合,使遥感影像传输速度和线路资源利用率均有一定提高。     

基于蚁群算法的离散救援问题出救点选址研究

为解决应急物流中的出救点选址问题,建立了相应数学模型,引入蚁群算法解决问题。多数应急物流可以归为点对点的支援问题,出救点的设置应该在保证出救有效的条件下使出救点最少、救援时间最短,属于双层规划问题。双层规划问题是NP难题,可以应用蚁群算法解决。出救点选址问题在蚁群算法中可以视为蚁群的聚类,通过对信息素衰减及相邻蚂蚁的吸引作为启发因子,可以得到蚁群的聚类效果。实验结果表明,基于蚁群算法的选址问题解决方案能获得理想的选址效果,收敛速度较快。