蚁群生成树算法研究

应用蚁群生成树算法搜索了有34个节点的连接图的生成树,并采用正交设计法和均匀设计法进行了参数优化配置方法研究。结果表明:对于参数较多的蚁群算法,应用正交设计法和均匀设计法进行参数优化配置是一种可行且有效的途径,可有效提高蚁群算法的收敛速度,在求解精度上也有一定优势;充分发挥人类智能与仿生物智能的各自优势是克服单纯靠智能优化方法随机搜索缺点的关键;当蚂蚁数目为100、信息素相对重要性因素为0.3、信息素衰减系数为3.6、信息素挥发系数为0.4、信息素增加强度系数为14时,蚁群生成树算法效果最佳。     

基于蚁群算法自整定PID的风电系统最大风能追踪研究

针对风力发电系统处于欠功率阶段时,风能利用系数须保持在最大值的问题,以欠功率阶段的最大风能追踪为研究重点,对风力机捕获风能的过程进行理论分析,提出了一种基于蚁群算法自整定PID的最大风能追踪控制策略,利用蚁群算法的全局优化能力优化PID的3个参数,给出了该算法的基本思想以及具体实现步骤,设计了蚁群算法自整定PID控制器,搭建了系统仿真图,并对其进行相应的仿真分析;仿真结果表明,与传统的PID控制策略相比,该控制策略使控制系统具有良好的动态响应能力,提高了风电系统的控制精度、风能利用率、输出功率,实现了机组的优化运行.     

糖厂澄清工段生产指标建模及操作参数优化

针对亚法糖厂澄清工段清汁色值和清汁残硫量难以在线测量的问题,提出了一种基于人工蜂群优化的在线极限学习机软测量方法;先用核主元分析法确定影响清汁质量的关键参数,建立基于在线极限学习机的软测量模型;同时利用人工蜂群算法对在线极限学习机的隐层参数进行寻优,优化所建模型;最后,使用带约束的粒子群对软测量模型进行优化求解,得到典型工况下的最优操作设定值,为后续工况操作提供参考依据;仿真结果表明,基于人工蜂群优化的在线极限学习机模型能够准确地预测清汁色值和残硫量,同时基于此模型优化的操作参数设定值能够达到期望的指标.     

蚁群投影寻踪回归在广西柳州市防洪体系综合评价中的应用

在分析我国城市防洪体系安全评价的指标体系及其等级标准的基础上,提出了一种基于投影寻踪回归的城市防洪体系综合评价模型,采用蚁群算法对评价模型进行优化,并将该模型应用于广西壮族自治区柳州市防洪体系的评价。研究表明,投影寻踪回归分析法,避免了传统评价方法由于主观原因造成的误差,评价结果符合客观实际、方法简单,为城市防洪体系的评价提供了新途径。     

A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational biology.The modeling of the protein backbone represents the initial phase of a protein structure prediction process.Using a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein backbone.The parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy functions.All free modeling targets in CASP8/9 are used to evaluate the performance of the method.For 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top 10.For 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.     

A novel artificial bee colony algorithm with Powell's method

Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.     

Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic optimization problems (DOPs). This is because once the algorithm converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to a new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution to address this problem is to increase the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants schemes. In this paper, an ACO framework for dynamic environments is proposed where different immigrants schemes, including random immigrants, elitism-based immigrants, and memory-based immigrants, are integrated into ACO algorithms for solving DOPs. From this framework, three ACO algorithms, where immigrant ants are generated using the aforementioned immigrants schemes and replace existing ants in the current population, are proposed and investigated. Moreover, two novel types of dynamic travelling salesman problems (DTSPs) with traffic factors, i.e., under random and cyclic dynamic environments, are proposed for the experimental study. The experimental results based on different DTSP test cases show that each proposed algorithm performs well on different environmental cases and that the proposed algorithms outperform several other peer ACO algorithms.     

基于ACC-RBF的水布垭面板堆石坝参数反演分析

蚁群聚类径向基函数(ACC-RBF)神经网络是将蚁群聚类算法和径向基函数神经网络组合运用的一种新型神经网络模型,把该网络用于水布垭高面板坝堆石体的多参数反演问题,在室内试验参数的基础上用有限元计算获得学习样本,采用该网络对坝体堆石料的邓肯E-B模型参数进行反演分析,用反演所得参数结合三维非线性有限元计算坝体应力变形,并与实测资料比较,得出很接近的结论.     

奖惩蚁群算法

由于传统蚁群算法所采用的是随机概率搜索策略,收敛速度慢是其主要问题。为了提高算法的收敛速度,这里提出一种带奖惩策略的蚁群算法（PPACO）。新算法中,每次循环中发现的较优解都被挑选出来加以奖励,而普通解则被惩罚,这样就加快了较优路径和普通路径上信息素的差异;另外,为了不使这种差异对算法产生过多的影响,所有路径上的信息素都被限制在一定的范围[τmin,τmax]内,同时,信息素的挥发系数被设为相对较高值。通过典型模拟实验证明,新算法对解决复杂组合优化问题非常有效。     

具有感觉适应功能蚁群算法的机器人路径规划

用传统蚁群算法进行机器人路径规划具有收敛速度慢,容易陷入局部最优等缺点。为此,通过模拟蚂蚁的感觉特征,提出了一种具有感觉适应功能的蚁群算法,并应用到机器人路径规划中。算法设置蚂蚁的感觉能力随着在高强度信息素节点上的行走而降低,在快速收敛的同时保证搜索多样性。大量仿真实验表明,该算法有较强的搜索能力,不易陷入局部最优,即使在障碍非常复杂的环境下使用该算法,也能快速收敛到一条全局优化路径。