基于微粒群算法的辽宁省可持续农业产业结构优化配置研究

针对辽宁省农业产业结构中存在的问题,从经济、生态、社会三方面综合考察,建立了该区域的可持续农业产业结构优化模型,并利用改进的微粒群多目标优化算法对模型进行了求解,为辽宁省以及相似区域的农业产业结构调整提供了理论依据.     

基于微粒群-马尔科夫复合模型的生态空间预测模拟——以长株潭城市群为例

生态空间具有重要的生态功能,对生态空间进行科学预测模拟可为保护国土空间生态安全提供决策依据。利用Arc GIS及MATLAB软件,在生态空间风险评价的基础上构建了微粒群-马尔科夫复合模型,并以长株潭城市群为研究区,基于2013年土地利用现状数据,对2020年的生态空间进行了预测模拟,最后在此基础上提出了生态空间重构的基本思路。结果表明:1)微粒群-马尔科夫复合模型(PSO-Markov)构建的基本步骤为:第一步:粒子的选择与设计,以2000 m×2000 m的正方形单元作为基本粒子。第二步:粒子的初始化设定,根据生态空间风险由低到高的原则进行选择。第三步:适应度函数的建立,用生态空间的风险值来确定生态空间的空间格局。第四步:空间位置的更新,根据自身的历史最优值及粒子群的全局最优值进行速度和位置更新。2)微粒群-马尔科夫复合模型(PSO-Markov)是一种土地利用格局预测的新途径,生态空间的数量规模可以通过改进后的马尔科夫模型进行预测,生态空间的格局可以通过微粒群模型进行预测。3)微粒群-马尔科夫复合模型具有4个特点:第一、数量预测较为合理。第二、搜索范围大、较好地考虑到局部对全局的影响。第三、受问题维数变化影响小,在求解多目标问题时具有明显优势。第四、收敛时间短、运算速度快、易于实现。4)2020年,长株潭城市群的生态空间总体数量减少,其中林地和未利用地面积变化最明显,空间变化主要集中分布在西南部地区。生态空间总面积减小的主要原因是建设用地的扩张。因此,要控制城市群的人口密度,优化城市群生产—生活—生态的数量结构及空间布局,尤其要合理规划与利用城市建设用地,充分发挥水体与未利用地的生态价值,重点保护好生态源地、廊道及关键结点,构建结构合理、功能齐全的生态网络系统,提高系统的生态服务价值功能,要在规划的指导下合理调整城市群的城乡局部空间结构,保护生态环境,提高生境质量和景观多样性。这是今后一段时期面临的主要任务。     

复杂生态系统稳定性的补偿模糊神经网络建模

以环境因素、植物功能群、植食动物功能群、捕食动物功能群和分解者功能群为5组输入。建立实现模糊参数自动调整和补偿模糊推理的复杂生态系统稳定性的补偿模糊神经网络模型,评价复杂生态系统的稳定性,通过计算机仿真,验证了所提出的方法具有学习效率高、评价精确度高、泛化能力强的优点．     

肿瘤亚型识别研究中智能算法的应用

目的:为解决肿瘤亚型识别过程中易出现的维数灾难和过拟合问题,提出了一种改进的粒子群BP神经网络集成算法。方法:算法采用欧式距离和互信息来初步过滤冗余基因,之后用Relief算法进一步处理,得到候选特征基因集合。采用BP神经网络作为基分类器,将特征基因提取与分类器训练相结合,改进的粒子群对其权值和阈值进行全局搜索优化。结果:当隐含层神经元个数为5时,候选特征基因个数为110时,QPSO/BP算法全局优化和搜索,此时的分类准确率最高。结论:该算法不但提高了肿瘤分型识别的准确率,而且降低了学习的复杂度。     

L-缬氨酸发酵培养基的神经网络建模与遗传算法优化

运用神经网络对L-缬氨酸发酵培养基组成进行建模,在神经网络模型的基础上采用遗传算法对培养基组成进行优化,得到最佳发酵培养基组成.结果表明,运用神经网络并结合遗传算法是一种行之有效的优化方法.按最佳发酵培养基组成进行发酵实验64h,可在发酵液中积累L-缬氨酸28.5g/L.     

农业生命周期评价研究进展

作为评价产品系统全链条环境影响的有效工具,生命周期评价（LCA）方法已广泛用于工业领域。农业领域也面临着高强度的资源和环境压力,LCA在农业领域的应用应运而生。旨在综述已有农业LCA研究的基础上,鉴别农业LCA应用存在的问题,并为农业LCA未来的发展提出建议。目前农业LCA存在系统边界和功能单位界定不明晰、缺少区域清单数据库、生命周期环境影响评价模型（LCIA）不能准确反映农业系统环境影响、结果解释存在误区等方面的问题。为了科学准确地衡量农业系统的环境影响,促进农业系统的可持续发展,文章认为农业LCA应该从以下几个方面加强研究,即科学界定评价的参照系、系统边界的扩大及功能单位的合理选取、区域异质性数据库构建与LCIA模型开发、基于组织农业LCA的开发以及对于利益相关者行为的研究。     

新型粒子群优化算法在多序列联配中的应用

将粒子群优化算法应用于序列联配,提出了一种改进的粒子群优化算法,该算法在粒子群的进化过程中根据粒子的适应值动态地调整粒子群的惯性权重与粒子群飞行速度范围,提高了算法的收敛速度和收敛精度;针对PSO算法可能出现的早熟现象,引入重新初始化机制,增强了算法的搜索能力,实验表明该算法是有效的。     

基于SOM神经网络的白河林业局森林健康分等评价

将自组织特征映射(SOM)神经网络引入森林健康评价领域,与地理信息系统技术(GIS)相结合,基于森林经营小班尺度,对长白山白河林业局3个主要森林类型(阔叶混交林、针阔混交林、长白落叶松林)的森林健康状况进行定量评价,并分析了不同平均年龄段、不同平均树高、不同郁闭度森林小班的健康状况.结果表明:SOM神经网络是自动化定量评价森林健康的一个较先进的方法,其用于森林健康分等评价的最大优点是不需要知道分等类别的先验知识,不需要事先人为确定分等评价因素指标的权重,能有效地克服主观因素的干扰,使分等结果更加客观准确;不同森林类型健康等级状况的比例排序为阔叶混交林Ⅲ>Ⅱ>Ⅰ>Ⅳ>Ⅴ,针阔混交林Ⅱ>Ⅳ>Ⅰ>Ⅲ>Ⅴ,长白落叶松林Ⅰ>Ⅱ>Ⅲ>Ⅴ>Ⅳ;相对来说,森林小班平均年龄越大、平均树高越高、郁闭度越高,呈健康状况的小班比例也越高.以上评价结果可为白河林业局的森林可持续经营和多功能利用提供理论支撑.     

基于灰狼优化算法的蛋白质二级结构分类

针对传统方法在蛋白质二级结构分类中精度低的问题,介绍了一种基于灰狼优化算法的卷积神经网络图像分类算法.首先,选取卷积神经网络模型中所需优化的参数,并且初始化灰狼优化算法的迭代次数、灰狼数量、搜索边界和空间维数;其次,计算优化参数的个体适应度函数,对个体适应度进行排序,确定历史最优解、优解和次优解,更新灰狼的位置;最后,...     

区域自然保护区群规划——以秦岭山系为例

自然保护区群的构建对于优化我国自然保护区空间布局、提高自然保护区的保护效果具有重要意义。以大熊猫为主要研究对象,以秦岭山系为研究区域,以生境评价与通达性分析为主要研究方法,探讨了自然保护区群合理布局与功能区优化的程序、内容与方法。研究表明,目前秦岭山系已建与筹建的大熊猫自然保护区17个,自然保护区群初步形成,保护了50%的大熊猫生境,但东部的3个自然保护区尚未全部相连,且整个自然保护区核心区被隔离为20部分,大大影响了自然保护区效果的发挥。为优化自然保护区的布局,建议新建湑水河与锦鸡梁两个自然保护区,以大熊猫居群为基本单位将核心区调整为4个,并且通过3个连接区的建设加强各大熊猫居群的连通性,以促进大熊猫种群间的交流与迁徙。研究结果可为秦岭山系自然保护区建设提供思路与依据,对其它地区自然保护区群的建立及全国自然保护区网络的优化也具有借鉴意义。     

A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved–such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.     

中国农业生态效率评价方法与实证——基于非期望产出的SBM模型分析

生态效率是衡量经济与资源环境协调发展的重要指标.基于将农业面源污染作为非期望产出的考量,在对传统DEA模型进行修正的基础上,采用非径向、非角度的SBM模型对中国30个省份的农业生态效率进行了测算,并给出了农业生态效率的改善途径.研究结果表明:1998-2009年中国农业生态效率虽然呈现缓慢上升态势,但整体仍处于较低水平,除北京、上海、海南、重庆外,其余省市都需要改变投入和产出来优化农业生态效率;资源的过度消耗和环境污染物的过量排放是农业生态效率损失的主要原因.提高资源利用效率、降低资源消耗量和环境污染物的排放量是农业生态效率改善的重要途径.     

A new particle swarm optimization algorithm for noisy optimization problems

We propose a new particle swarm optimization algorithm for problems where objective functions are subject to zero-mean, independent, and identically distributed stochastic noise. While particle swarm optimization has been successfully applied to solve many complex deterministic nonlinear optimization problems, straightforward applications of particle swarm optimization to noisy optimization problems are subject to failure because the noise in objective function values can lead the algorithm to incorrectly identify positions as the global/personal best positions. Instead of having the entire swarm follow a global best position based on the sample average of objective function values, the proposed new algorithm works with a set of statistically global best positions that include one or more positions with objective function values that are statistically equivalent, which is achieved using a combination of statistical subset selection and clustering analysis. The new PSO algorithm can be seamlessly integrated with adaptive resampling procedures to enhance the capability of PSO to cope with noisy objective functions. Numerical experiments demonstrate that the new algorithm is able to consistently find better solutions than the canonical particle swarm optimization algorithm in the presence of stochastic noise in objective function values with different resampling procedures.     

Set-based particle swarm optimization applied to the multidimensional knapsack problem

Particle swarm optimization algorithms have been successfully applied to discrete/valued optimization problems. However, in many cases the algorithms have been tailored specifically for the problem at hand. This paper proposes a generic set-based particle swarm optimization algorithm for use in discrete-valued optimization problems that can be formulated as set-based problems. A detailed sensitivity analysis of the parameters of the algorithm is conducted. The performance of the proposed algorithm is then compared against three other discrete particle swarm optimization algorithms from literature using the multidimensional knapsack problem and is shown to statistically outperform the existing algorithms.     

“三生空间”视角下三峡库区土地利用转型的生态系统服务价值效应

明晰社会经济快速转型背景下的“三生空间”演变过程及其生态系统服务效应,对国土空间优化和区域可持续发展具有重要意义。以1980、2000、2018年土地利用遥感监测数据为基础,基于“三生空间”视角,采用地学信息图谱刻画了三峡库区土地利用转型过程,并通过生态系统服务贡献值测度了土地利用转型的生态环境效应。结果表明: 1980—2018年,三峡库区工业生产空间和生活空间面积不断增加,而农业生产空间和生态空间面积逐渐减少;从图谱转移视角分析,1980—2000年间,土地利用格局相对稳定,以未发生转移的图谱单元占主导,而2000—2018年土地利用转型复杂,以农业生产空间与林地、草地生态空间相互转化占主导,城镇化、工业化进程加快是导致农业生产空间、生态空间面积萎缩的主要原因;三峡库区生态系统服务价值在时间上呈先缓慢降低后迅速增加的趋势,在空间上呈“东部高-西部低”的分布特征;生态保护修复工程促进了生态系统服务价值正效应,但经济社会快速发展挤占了大量的农业生产空间、生态空间,加之农田复耕等行为在一定程度上导致了生态系统服务价值负效应。     

Phytodiversity of temperate permanent grasslands: ecosystem services for agriculture and livestock management for diversity conservation

Plant diversity has been reported to increase productivity. Farming practices aiming at conserving or increasing plant diversity are, however, usually less profitable than conventional ones. In this review, we aim to find reasons for this discrepancy, discuss ecosystem services of grassland phytodiversity that are useful for farmers, and ways of livestock management most beneficial for diversity. Under agricultural conditions, a clear effect of species richness on a site’s primary or secondary production has not yet been demonstrated. Reasons could be that species numbers in permanent grassland are above the threshold of five species found effective in experimental plots or that the conditions are more in equilibrium with management than in weeded experimental plots. Other diversity effects on production stability, nutrient and water retention or product quality might convince farmers to increase diversity. However, these should be tested in agricultural situations, as most research has again been carried out in experimental plots. To enhance phytodiversity, grazing has been found superior over mowing, as selective grazing, treading and excreta deposition increase the heterogeneity of a sward and thus the niches available. Especially rotational grazing with intermediate intensity may be advantageous for phytodiversity. However, complex interactions between environmental conditions, sward composition, management and livestock behaviour make it difficult to forecast grazing effects. Thus, ecological and agricultural researchers should cooperate more, e.g. either in interdisciplinary projects or by hiring researchers from the respective other profession and thus diversifying research groups, in order to integrate agricultural management into biodiversity research and biodiversity measurements into agricultural research to advance our understanding of how to make conservation and enhancement of grassland phytodiversity both feasible and sustainable.     

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS²Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS²O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS²O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS²O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.     

PseudoMLSA: a database for multigenic sequence analysis of <Emphasis Type="Italic">Pseudomonas</Emphasis> species

Background  

The genus Pseudomonas comprises more than 100 species of environmental, clinical, agricultural, and biotechnological interest. Although, the recommended method for discriminating bacterial species is DNA-DNA hybridisation, alternative techniques based on multigenic sequence analysis are becoming a common practice in bacterial species discrimination studies. Since there is not a general criterion for determining which genes are more useful for species resolution; the number of strains and genes analysed is increasing continuously. As a result, sequences of different genes are dispersed throughout several databases. This sequence information needs to be collected in a common database, in order to be useful for future identification-based projects.     

Financial and economic aspects of environmental management for vector control

Short term economic benefits of resource development generally carry more weight in political decision-making than the adverse health effects such projects may have in the medium and long term. This problem is particularly acute in the case of water resource development projects which can often promote the prevalence and incidence of certain vector-borne diseases such as schistosomiasis and malaria. And while the agricultural sector is usually strengthened by production increase resulting from water resource development, the health sector may ultimately have to pick up the bill for disease control programmes which become necessary as a consequence. At its sixth meeting in Geneva, the WHO/FAO/UNEP Panel of Experts on Environmental Management for Vector Control (PEEM) discussed the financial and economic aspects of environmental management, and its cost effectiveness as a vector control measure. Their report is now available and in this article, Robert Bos and Anne Mills discuss some of the key findings.     

Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms

The performance of optimization algorithms, including those based on swarm intelligence, depends on the values assigned to their parameters. To obtain high performance, these parameters must be fine-tuned. Since many parameters can take real values or integer values from a large domain, it is often possible to treat the tuning problem as a continuous optimization problem. In this article, we study the performance of a number of prominent continuous optimization algorithms for parameter tuning using various case studies from the swarm intelligence literature. The continuous optimization algorithms that we study are enhanced to handle the stochastic nature of the tuning problem. In particular, we introduce a new post-selection mechanism that uses F-Race in the final phase of the tuning process to select the best among elite parameter configurations. We also examine the parameter space of the swarm intelligence algorithms that we consider in our study, and we show that by fine-tuning their parameters one can obtain substantial improvements over default configurations.