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.     

Bacteria foraging optimization in antenna engineering: An application to array fault finding

Finding fault elements in linear antenna arrays using bacteria foraging optimization (BFO) is presented. One of the better options of array diagnosis is to perform it by measuring the radiated field, because in this case, removal of the array from its working site is not required and thereby not interrupting its normal operation. This task of fault finding from far‐field data is designed as an optimization problem where the difference between the far‐field power pattern obtained for a given configuration of failed element(s) and the measured one is minimized w. r. t. the excitations of the array elements. This set of excitations on comparison with the excitations of the original array gives the idea of the fault position and their type, such as either complete fault or partial fault. BFO being relatively new to microwave community when compared with other soft‐computing techniques, its performance was observed w. r. t. time of computation and convergence of the iterative process. Possibility of finding the faults from random sample points and use of minimum number of sample points for array fault finding are the novelties of the present work. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Tracking variable environments: There is more than one kind of memory

Three kinds of memory help herbivores track changes in the environment. The first is the collective memory of the species with genetic instructions that have been shaped by the environment through millennia. This includes skin and gut defense systems. Auditory and visual stimuli and sensations of pain impinge upon the skin defense system that evolved in response to predation. The taste of food and the sensations of nausea and satiety are an integral part of the gut defense system that evolved in response to toxins and nutrients in plants. The second kind of memory in social mammals is represented by the mother, a source of transgenerational knowledge, who increases efficiency and reduces risk of learning about foods and environments. The third kind of memory is acquired by individual experience. Postingestive feedback from nutrients and toxins enables individuals to experience the consequences of food ingestion and to adjust food preference and selection commensurate with a food's utility. The three memories interact, each linking the past to the present, and collectively shape the present and future of every individual. Thus, the dynamics of foraging involves appreciating the uniqueness of individuals and subgroups of animals, each with their own genetic and behavioral history, and recognizing that foraging behaviors may not be stable, optimal, or even predictable in the conventional sense.     

人工蜂群算法综述

近年来群智能算法发展较为迅速并解决了很多大规模的复杂问题。人工蜂群算法是一种新型的群智能算法, 以其很强的全局收敛性、贪婪启发式的搜索特征以及求解问题的快速性等优越的性能受到广泛关注。简单介绍了人工蜂群算 法提出的生物学背景;由蜜蜂觅食行为与现实问题的求解类比给出了该算法的建模思想;并详细介绍了人工蜂群算法实现的 算法模型;从基于算法的改进以及基于算法的应用两方面讨论了近年来很多学者对人工蜂群算法研究的现状;最后对人工蜂群 算法的研究进行展望,从算法的弱点分析提出了该算法改进的方向以及进一步应用的领域。     

A comparison of swarm intelligence algorithms for structural engineering optimization

This paper compares the performance of three swarm intelligence algorithms for the optimization of hard engineering problems. The algorithms tested were bacterial foraging optimization (BFO), particle swarm optimization (PSO), and artificial bee colony (ABC). Besides the regular BFO, two other variants reported in the literature were also included in the study: adaptive BFO and swarming BFO. Both PSO and ABC were tested using the regular algorithm and variants that include explosion (mass extinction). Three optimization problems of structural engineering were used: minimization of the cost of a welded beam, minimization of the construction cost of a pressure vessel, and minimization of the total weight of a 10‐bar plane truss. All problems are strongly constrained. The algorithms were evaluated using two criteria: quality of solutions and the number of function evaluations. The results show that PSO presented the best balance between these two criteria. For the optimization problems approached in this paper, we can also conclude that the explosion procedure resulted in no significant improvements. Copyright © 2012 John Wiley & Sons, Ltd.     

The central executive as a search process: Priming exploration and exploitation across domains.

The trade-off between exploration and exploitation is common to a wide variety of problems involving search in space and mind. The prevalence of this trade-off and its neurological underpinnings led us to propose domain-general cognitive search processes (Hills, Todd, & Goldstone, 2008). We propose further that these are consistent with the idea of a central executive search process that combines goal-handling across subgoal hierarchies. In the present study, we investigate 3 aspects of this proposal. First, the existence of a unitary central executive search process should allow priming from 1 search task to another and at multiple hierarchical levels. We confirm this by showing cross-domain priming from a spatial search task to 2 different cognitive levels within a lexical search task. Second, given the neural basis of the proposed generalized cognitive search process and the evidence that the central executive is primarily engaged during complex tasks, we hypothesize that priming should require search in the sense of a self-regulated making and testing of sequential predictions about the world. This was confirmed by showing that when participants were allowed to collect spatial resources without searching for them, no priming occurred. Finally, we provide a mechanism for the underlying search process and investigate 3 alternative hypotheses for subgoal hierarchies using the central executive as a search process model (CESP). CESP envisions the central executive as having both emergent and unitary processes, with one of its roles being a generalized cognitive search process that navigates goal hierarchies by mediating persistence on and switching between subgoals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effects of rumen fill on short-term ingestive behavior and circulating concentrations of ghrelin, insulin, and glucose of dairy cows foraging vegetative micro-swards

The objective of this study was to investigate changes in foraging behavior, hunger-related hormones, and metabolites of dairy cows in response to short-term variations in rumen fill (RF). The effect of RF on intake rate, jaw movements, bite rate and dimensions, and concentrations of plasma ghrelin, and serum insulin and glucose were measured in 4 rumen-cannulated lactating dairy cows (612 ± 68 kg, empty live weight; 237 ± 29 d in milk) foraging micro-swards of vegetative orchardgrass (Dactylis glomerata L.). The treatments compared were the removal of different proportions of total rumen contents: 1.00 (RF0), 0.66 (RF33), 0.33 (RF66), or 0 (RF100). Treatments were randomly applied 2 h before foraging sessions in a 4 × 4 Latin square design. Micro-swards were weighed before and after foraging sessions. Cows were allowed to take a maximum of 15 bites with no time restriction. Eating time, intake rate, total jaw movements, and bite mass, depth, area, and rate were determined. Plasma was analyzed for ghrelin and serum for insulin and glucose immediately before and 2 h after the treatments were applied. Intake rate, bite mass, and bite area increased, whereas bite depth decreased as RF decreased. The RF did not affect bite rate or total jaw movements. Decreasing RF resulted in increased plasma concentrations of ghrelin and tended to increase serum insulin, with reduced concentrations of serum glucose. Incremental variation in plasma ghrelin and serum insulin correlated with bite depth and mass, whereas changes in serum glucose correlated with intake rate, bite area, depth and mass, as well as with herbage intake per jaw movement. The present study elucidates some of the underlying endocrine physiology of cattle with short-term temporal variations of RF and their effects on some components of foraging behavior.     

随机扰动下多源群体觅食系统建模与仿真

群集智能是指复杂的集体智能来自简单个体之间以及个体同环境之间的相互作用. 通常对群集智能的研究主要借助于群居生物行为的观察. 蚁群觅食行为是研究简单个体产生复杂行为的一个典型的例子. 首先建立群体觅食宏观序参数模型. 模型考虑了食物源的量和分布以及环境噪声对个体决策的随机影响. 给出2个食物源下系统模型的数值解,表明在较大的噪声影响下,系统有一定的概率会脱离最优解,到达次优解. 在Starlogo仿真平台下的实验结果表明,觅食蚂蚁的数量同任务完成时间以及碰撞频率之间呈现出幂指数关系. 这对自组织系统和群集智能的研究有一定的理论意义,并可以用来指导设计更加有效、适应、可靠的智能系统.     

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)     

多策略改进的天鹰优化算法及其应用

为改善天鹰优化算法(aquila optimizer, AO)在求解复杂优化问题时存在易陷入局部最优等不足,提出一种多策略改进的天鹰优化算法(multi-strategy improved aquila optimizer, MIAO)。首先,提出镜像单纯形法策略扩大天鹰搜索范围,提升种群多样性和逃离桎梏能力;其次,在天鹰算法的X3阶段融入社会自由觅食策略,摆脱全局平均值的束缚,提升迭代后期天鹰个体的多样性;同时,将阶梯步进策略引入X4阶段,保证当前优势个体加快向全局最优前进的趋势,增加收敛速度;最后,改进原有开发机制,提升算法寻优能力。对10个常用基准函数以及CEC2017部分函数进行寻优实验,实验结果与Wilcoxon符号秩和检验结果均表明改进算法具有更好的寻优精度、收敛性能和稳定性。另外,通过一个机械优化设计实验进行测试分析,进一步验证所改进算法的优越性和实用性。