基于记忆模拟退火和A*算法的农业机器人 遍历路径规划

【目的】解决农业机器人大田作业时遍历路径规划的问题。【方法】提出一种记忆模拟退火与A*算法相结合的遍历算法。首先通过记忆模拟退火算法搜索出任务最优目标点行走顺序,然后使用A*算法进行跨区域衔接路径规划。【结果】仿真试验结果表明,该算法规划的遍历路径曼哈顿距离比传统模拟退火算法减少了9.4%,遍历路径覆盖率能达到100%,重复率控制为4.2%。【结论】记忆模拟退火通过为传统模拟退火算法增加记忆器,增强了跳出局部最优陷阱的能力,提高了算法所得解的质量。该研究结果可为农业机器人遍历路径规划提供理论基础。     

放牧和模拟降水对短花针茅荒漠草原植物功能群多样性的影响

为探究放牧和降水变化对植物功能群的影响,本文以内蒙古短花针茅(Stipa breviflora)荒漠草原为研究对象,开展了野外控制试验。试验设置围封和适度放牧,分别进行模拟降水处理(减少降水量50%、自然降水、增加降水量50%、增加降水量100%),并在每年8月观测植物群落组成。结果表明：减水会降低植物功能群多样性、生物量和物种数量,增水有利于植物功能群多样性的提高。多年生丛生禾草多样性随降水量的增加而升高。同时,对降水梯度和功能群多样性进行拟合分析发现,降水量低于年降水量16%则不利于除灌木、半灌木外其他功能群植物的生长。围封草地增水67%～79%时,功能群的Simpson指数、Shannon-Wiener指数、Pielou指数值均较高,功能群多样性较高;由于放牧提高了植物的水分利用效率,放牧地各指数在增水26%～50%时较高,功能群多样性较高。综上,适度的放牧及水分补充有利于提高植物功能群多样性。     

基于近红外光谱的沼液挥发性脂肪酸含量快速检测

挥发性脂肪酸（Volatile Fatty Acids，VFA）作为厌氧发酵过程的重要中间产物，其在厌氧反应器中的累积能够反映出产甲烷菌的不活跃状态或厌氧发酵条件的恶化。为了实现对农牧废弃物厌氧发酵进行过程分析和状态监控，将近红外光谱（Near Infrared Spectroscopy，NIRS）与偏最小二乘（Partial Least Squares，PLS）相结合构建玉米秸秆和畜禽粪便厌氧发酵液乙酸、丙酸和总酸含量快速检测模型。将竞争自适应重加权采样法（Competitive Adaptive Reweighted Sampling，CARS）与遗传模拟退火算法（Genetic Simulated Annealing algorithm，GSA）相结合构建CARS-GSA算法对沼液中的乙酸、丙酸和总酸进行特征波长优选，原始光谱数据1 557个波长点经预处理和波长优选后，得到乙酸、丙酸和总酸特征波长变量分别为135、101和245个，建立的回归模型验证决定系数分别为0.988、0.923和0.886，预测均方根误差（Root Mean Squared Error of Prediction，RMSEP）分别为0.111、0.120和0.727，相对分析误差分别为9.685、3.685和3.484，与全谱建模相比RMSEP分别减少了17.78%、15.49%和1.22%，能够满足农牧废弃物厌氧发酵过程发酵液中乙酸和丙酸含量的快速检测需求，基本满足总酸的检测需求。结果表明，通过构建CARS-GSA算法优选乙酸、丙酸和总酸的敏感波长变量，参与建模的波长点数量显著减少，有效降低了变量维度和模型复杂度，提升了回归模型检测精度和预测能力，为快速准确检测沼液VFA提供了新途径。     

菌株Bacillus sp.CLb芽孢漆酶对染料、模拟染料废水脱色的研究

[目的]研究芽孢杆菌菌株CLb的芽孢漆酶在介体的互作下对不同类型的染料及模拟染料废水的脱色效果。[方法]制备芽孢粗酶液,在介体的互作下,测定具有漆酶活性的粗酶液对活性亮蓝、活性黑、靛红和结晶紫的脱色效果,筛选出对染料脱色有促进作用的漆酶介体,并且研究芽孢漆酶在不同pH条件下对染料和模拟染料废水脱色的影响。[结果]菌株CLb的芽孢漆酶在无介体参与下只能使活性亮蓝和结晶紫脱色。在介体乙酰丁香酮（ACE）的作用下,芽孢漆酶对4种染料的脱色率均超过70%。在pH 7.0条件下,芽孢漆酶浓度为44.74 U/L和介体ACE浓度为1 mmol/L时对模拟染料废水的脱色效果最好,6 h脱色率超过80%。在介体乙酰丁香酮存在,pH 7.0的条件下,菌株CLb的芽孢漆酶对合成染料脱色效率高于pH 9.0时的脱色率。[结论]在介体乙酰丁香酮的存在下,芽孢漆酶对染料和模拟染料废水均具有较好的脱色效果。     

基于MOSA算法的高压共轨喷油器多目标仿真优化设计

建立了高压共轨喷油器的Matlab/Simulink仿真模型,采用多目标模拟退火MOSA优化算法作为寻优策略,以针阀开启延迟和关闭延迟为目标函数进行高压共轨喷油器的关键结构参数多目标仿真优化设计。实验结果表明:优化后的高压共轨喷油器针阀开启延迟时间降低16.7%,达到0.25 ms;关闭延迟时间降低15.2%,达到0.78 ms。     

基于模拟退火算法优化给水管线造价公式研究

给水管线造价公式的精确性对工程经济分析与给水管网优化的科学性和经济性有重大影响。通过五个地区的铸铁管综合单价数据,采用最小二乘法对各组数据进行多项式一至十次的拟合,确定了给水管线造价公式的最佳拟合次数为三次;进一步采用模拟退火算法对拟合的多项式参数进行优化计算,求解给水管线造价公式。结果表明:凭借模拟退火算法随机全局搜索模式以及不受函数性质影响的优势,能够克服传统算法难以求解多阶导数的困难,提高公式拟合精度。     

基于模拟退火算法的减速器多目标优化设计

提出了采用模拟退火算法进行减速器参数的全局优化的方法;针对斜齿轮减速器优化设计的多参数、多目标、多约束特点,建立了多目标优化数学模型;应用层次分析法计算了各目标权重。仿真结果表明,优化参数比原设计参数的优化解要好,说明模拟退火算法用于减速器优化设计是有效、可行的。     

Spatial optimization by simulated annealing and linear programming

Forest management planning comprises selection among treatment alternatives in management units. A traditional linear programming (LP) approach may effectively account for a profit maximization objective combined with sustainability constraints, e.g. on the temporal distribution of harvest volume flows, cash‐flow, and net present value development, but it fails to account for spatial constraints, especially those associated with final felling. By applying a simulated annealing adjacency model based on net present value maximization and combined with an LP consequence computation model, it is possible to delineate optimal strategies of final felling scheduling. Evaluation is made of the trade‐off between (1) the incremental cost (determined by use of the LP model) of an optimal adjacency model solution, and (2) the potential damage cost resulting from adjacency characteristics such as windthrow and bark injuries. The decision support system may contribute significantly to reduce damage costs and may improve the reliability of forest management planning.     

Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

As water resources are limited and the demand for agricultural products increases, it becomes increasingly important to use irrigation water optimally. At a farm scale, farmer's have a particularly strong incentive to optimize their irrigation water use when the volume of water available over a season is production limiting. In this situation, a farmer's goal is to maximize farm profit, by adjusting when and where irrigation water is used. However, making the very best decisions about when and where to irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation season. Future rainfall uncertainty further complicates decisions on when and which crops should be subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water availability is limited. Previous optimal scheduling methods generally use stochastic dynamic programming, which requires over-simplistic plant models, limiting their practical usefulness. The CIS method improves on previous methods because it accommodates realistic plant models. Future farm profit (the objective function) is calculated using a time-series simulation model of the farm. Different irrigation management strategies are tested using the farm simulation model. The irrigation strategies are defined by a set of decision variables, and the decision variables are optimized using simulated annealing. The result of this optimization is an irrigation strategy that maximizes the expected future farm profit. This process is repeated several times during the irrigation season using the CIS method, and the optimal irrigation strategy is modified and improved using updated climate and soil moisture information. The ability of the CIS method to produce near optimal decisions was demonstrated by a comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS method can incorporate more realistic farm models than is possible when using stochastic dynamic programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results show that when seasonal water limit is the primary constraint on water availability, the CIS could increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with scheduling irrigation using current state of the art scheduling practice.