Text skimming: The process and effectiveness of foraging through text under time pressure.

Is Skim reading effective? How do readers allocate their attention selectively? The authors report 3 experiments that use expository texts and allow readers only enough time to read half of each document. Experiment 1 found that, relative to reading half the text, skimming improved memory for important ideas from a text but did not improve memory of less important details or of inferences made from information within the text. Experiment 2 found no advantage of skimming over reading the first or second half of every paragraph. Two final experiments using a hierarchical, Website-like layout of documents showed that the advantage of skimming found in Experiment 1 was dependent on the linkages between pages and, thus, the ease with which participants could navigate through the text. Data on page-by-page reading times and eye-tracking analyses from Experiment 2 indicated that Skim readers spent more time reading text that was earlier in the paragraph, toward the top of the page and in an earlier page of the document. These findings were interpreted as evidence in support of a “satisficing” account of skimming process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

采用PSO-BF算法的微电网多目标电能优化调度

考虑到微电网中各分布式电源的特点以及充分发挥分布式发电技术在经济、环境和能源方面的优势,在满足系统约束条件下,提出了一种综合考虑微电网的运行成本、电压偏差以及微电网削峰填谷能力的微电网多目标经济调度模型。同时针对粒子群算法(PSO)在求解优化问题时易陷入局部最优的缺点,提出一种粒子群-细菌觅食算法(PSO-BF)用于求解微电网电能优化调度问题。仿真结果表明该算法具有较强的全局、局部搜索能力且收敛速度快、精度高的特点。     

含分布式发电的改进BFO算法配电网无功优化

在含分布式电源的电网无功优化研究中,为了更有效地提高配电网性能,提出了一种改进细菌觅食算法(CP-BFO).以电网网损最小、负荷节点电压和发电机的无功出力约束作为综合目标函数,采用细菌觅食算法,在聚焦操作中引入粒子群变异算子,使算法具有良好的全局搜索能力,提高了算法的寻优效率.同时利用混沌原理对改进的细菌觅食算法的参数进行自适应调节,改善了算法的收敛性能.通过节点系统的仿真表明,CP-BFO算法在提高含分布式电源的智能电网电压质量与减少功率损耗的优化过程中具有可行性和有效性.     

基于细菌觅食特征改进粒子群算法优化SVM模型参数研究

针对粒子群算法优化SVM模型参数在进化后期容易陷入局部最优的问题,研究了细菌觅食趋利避害机制,提出了一种基于细菌觅食特性改进粒子群算法的方法,并将改进方法应用于优化SVM预测模型参数的研究;实验结果表明,该方法能够弥补粒子群算法在进化后期容易陷入局部最优的缺陷,具备更好的寻优性能。     

采用改进细菌觅食算法的风／光／储混合微电网电源优化配置

风能和太阳能具有随机性和波动性的特点,由分布式电源、储能装置、负荷组成的微电网协调运行与控制十分复杂,对孤岛运行的微电网合理地配置电源以提高供电可靠性、经济性是微电网规划建设的一个首要问题。将改进的细菌觅食算法（bacterial foraging algorithm,BFA）应用到解决风／光／储混合的全年孤岛运行的微电网电源优化配置问题中,建立了计及设备投资成本、运行和维护成本、燃料成本、环保折算成本的微电网电源优化配置模型,以年风速、气温、光照强度作为输入,根据不同的用户供电可靠性和备选电源要求,得到微电网电源的类型及其容量的最优方案,结果表明改进的细菌觅食算法具有全局最优搜索能力强、寻优速度快的特点。该方法可以全面评估各种分布式电源的经济性,根据微电网建设地点的气象条件、投资成本等降低微电网电源的冗余投资,满足用户定制的多样化可靠性要求。     

高比例新能源电网多源最优协同调频策略

随着大规模可再生能源对电网渗透率的不断增加,大型风光电站也开始参与到电网的调频当中.首先,建立了功率响应总偏差、调频里程支出最小化的多目标互补控制模型,以解决不同调频资源的动态功率分配问题.为解决该非线性优化问题,采用多目标蝠鲼觅食优化算法(multi-objective manta ray foraging opti...     

Modelling changes in trout habitat following stream restoration

Stream restoration was implemented on the Upper Arkansas River near Leadville, Colorado, to improve brown trout (Salmo trutta) populations. Metals pollution and channel disturbance associated with historic mining, land use, and water development degraded aquatic and riparian habitat. Changes in instream habitat quality following restoration were investigated with a before–after–control–impact study design. Baseline, as‐built, and effectiveness surveys were conducted in 2013, 2014, and 2016, respectively. Two‐dimensional hydrodynamic modelling with River2D was used to estimate weighted usable area (WUA) for adult, juvenile, fry, and spawning brown trout across a range of flows. WUA was calculated from habitat suitability curves for velocity, depth, and channel substrate. Foraging positions (FP) and habitat heterogeneity were also evaluated as indices of habitat quality. All results were analysed with analysis of variance. At impact sites, WUA increased by 12.2% from 2013 to 2014 but decreased by 10.2% from 2014 to 2016, whereas FP increased by 24.8% from 2013 to 2014 but decreased by 26.1% from 2014 to 2016. Spawning habitat increased 53.3% from 2014 to 2016 at impact sites. The 15.4% increase in depth variability from 2013 to 2016 indicates that habitat heterogeneity was enhanced at impact sites. No changes in WUA, FP, or habitat heterogeneity were observed at control sites. Although changes in WUA and FP suggest that initial habitat improvements were not sustained, increased spawning habitat and depth heterogeneity suggest otherwise. Our results highlight the value of monitoring strategies that utilize multiple lines of evidence to evaluate restoration effectiveness, inform adaptive management, and improve restoration practices.     

基于非均匀消除-扩散概率分布的情绪化细菌觅食算法

针对传统的细菌觅食算法在优化过程中存在的趋化步长的不确定性及消除-扩散概率的恒定性不足的问题，提出一种基于非均匀消除-扩散概率的情绪化细菌觅食算法，以解决高维度工程优化问题。首先，在趋化步骤中利用古斯分布搜索机制对细菌个体位置进行更新，以解决细菌因以随机方式在每个维度上游动或翻转而导致的搜索能力差及易陷入局部最优的问题，引入情绪感知因子，利用情绪智能的突变来实现自适应趋化步长，从而避免算法过早收敛；其次，针对细菌个体在消除-扩散过程中概率的恒定性，提出利用线性和非线性概率分布代替传统的常数分布以此实现非均匀分布的构想，通过引入动力因子随机值，限制未定义的搜索空间中的细菌个体，从而节省算法的计算成本。通过六个基准测试函数进行测试，测试结果表明，在计算成本较低的情况下，除针对Rosenbrock函数外，所提算法针对所有函数均具有较低的迭代次数及良好的优化质量，且算法收敛性对比结果表明所提的算法具有较好的收敛性。     

The Design and Role of Synthetic Creative Traits in Artificial Ant Colonies

Previous and current research on ant colonies have focused on the emergence of complex and sophisticated group-behaviors that are characteristic of the whole colony, starting from simple rules characterizing each individual ant. Examples of emergent behaviors include foraging and nest building. While social scientists, biologists, and physicists have focused on observing and analyzing the behavior of actual ants, researchers in computer science have investigated through modeling and simulation of synthetic ants on the computer, the emergent behavior in a collection of simple robots, simulated evolution of computer programs, and the formation of pheromone patterns during foraging under unlimited food supply. This paper has two objectives. First, it presents a systematic study, through behavior modeling and simulation, of the influence of the presence or absence of pheromone, the duration interval of pheromone, the extent of the food supply at the food sources, the size of the colony, and the search strategy employed during foraging, for a given geometry and a finite number of food sources, on the performance of the ant colony. The second and most important objective of this paper is to scientifically study the nature of creativity by modeling synthetic creativity in an ant colony, simulating it on a computer system, and measuring its impact on performance through innovative metric design. The study is motivated by leading thinkers, throughout time, who have strongly emphasized the importance of creativity over intelligence. While an exact and universal definition of creativity is elusive, the important characteristics include originality, intuition, and imagination. Creativity is best understood through its manifestations as novel and radically different ideas that transcend current knowledge and reasoning. In this study, two synthetic creativity traits are introduced into select individual ants of a colony by imparting to them a foraging behavior that is radically different from the normal behavior. Under the first trait, the creative ants choose to ignore the existing pheromone trails and search for food sources. Under the second trait, a creative ant shares its knowledge of food source location, after discovery, with all other creative ants, so that together they can focus solely on discovering new food sources. Analysis of the simulation results reveal that a creative trait coupled with the underlying parameters of the ant colony may cause the foraging completion time metric, i.e. the time to collect food from all sources, to be marginally better or weaker than the normal colony. Contrary to intuition, the completion time metric worsens when, in a fixed sized colony, the number of creative ants, relative to normal ants, becomes excessive. Furthermore, for a given finite geometry, finite food supply, and a fixed ratio of the number of creative to normal ants, the impact of creativity on the foraging performance is dependent on the underlying parameters, and may either continue to improve, remain unchanged, or decrease, as the size of the colony increases. Finally, while the creative ants expend, on an average, two to three times more energy than the normal ants, the ant colony with the creative ants discovers more food sources, in less time and, under certain circumstances, a creative colony discovers all food sources which a normal colony can never achieve.     

新型群智能优化算法综述

传统群智能算法在解决复杂实际多目标优化问题中存在不足,近年来学者提出诸多新型群智能算法,适用性强,在求解复杂实际问题中取得了较好的实验效果。以算法提出时间为主线,对新型群智能算法中细菌觅食优化算法、混合蛙跳算法、人工蜂群算法、萤火虫算法、布谷鸟搜索、果蝇优化算法和头脑风暴优化算法的改进及应用进行分析和综述,并对群智能算法未来的研究发展方向进行了探讨。