进化蚁群算法及其在湖泊富营养化评价中的应用

蚁群算法是近年提出的一种新型的仿生算法,已在许多组合优化问题中得到成功应用,但是传统蚁群算法解决连续优化问题的能力较差.为提高其解决连续优化问题的能力,拓宽应用范围,引入带可变邻域搜索项的进化策略对其进行改进,进而提出进化蚁群算法.随后从2个方面对进化蚁群算法的性能进行测试:①采用多个经典测试函数测试进化蚁群算法用于解决连续优化问题的效果;②将进化蚁群算法应用于千岛湖的富营养化程度评价,以测试该方法解决实际问题的效果.函数测试结果表明,进化蚁群算法可以成功用于解决连续优化问题,并且优化过程所需初始个体的数量少,优化速度快;千岛湖富营养化程度评价实例的结果表明,进化蚁群算法应用于湖泊富营养化评价是可行的,可用于解决实际问题.      

基于优化算法的弹药贮存寿命预测方法

目的 实现对缺失及不足的制导弹药贮存失效数据预测及补充的能力。方法 首先通过4种不同的预测算法（GA-BP、PSO-BP、GA-SVM、PSO-SVM）,对自然贮存条件下弹药贮存失效数据进行预测,其次根据最小二乘拟合法,实现弹药贮存寿命评估模型的构建,再通过寿命评估模型,计算出不同方法下对应的贮存寿命。结果 通过不同模型的构建,4种预测方法与无优化条件下均能实现弹药贮存失效数据的预测,并且在规定可靠度,GA-BP和PSO-BP预测精度比另外2种方法更低。结论 GA-SVM与PSO-SVM更适合弹药贮存失效数据的预测,且效果更好。     

不同空间尺度下危化品运输路径优化

为了解决化工园区危化品运输路径优化问题,研究了化工“园区间”与“园区内”2种空间尺度下危化品运输的影响因素差异性,提出以最小化运输阻抗、最小化运输风险和最小化风险敏感度为优化目标的不同空间尺度下危化品运输路径优化模型。通过目标量化确定每条路径上的目标值,将标准化后的目标值引入到Dijkstra算法和集成目标函数最优折衷路径的扩展标号法中,对危化品运输多目标优化模型进行求解。研究结果表明:不同空间尺度下的危化品运输最优路径不同,应根据危化品运输的实际情况对运输路径进行选择。最后,以平顶山某化工园区的抽象路网为例对优化模型进行验证。     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

基于Kriging模型的载人航天某装置结构优化设计研究

对于处在振动环境中的载人航天某装置复杂结构,设计过程中存在最小化质量、同时最大化一阶整体振动频率的多目标优化问题,可以利用多目标遗传算法(NSGA-II)求解得到Pareto最优解集,但利用NSGA-II进行求解计算量大,对于复杂结构更甚。为了节省计算时间,提出基于Kriging代理模型的复杂结构多目标优化设计方法,首先通过拉丁超立方采样获得样本点,然后通过样本点创建Kriging模型,最后利用多目标遗传算法求解代理模型得到Pareto最优解集,方便设计师从中选择最优解,优化结果表明本文所述方法合理有效。     

区域性应急物资储备级库间的协同选址模型研究

鉴于我国应急物资的多部门分散管理与分散储备的特点,研究了区域性应急物资储备体系构建中的应急物资储备级库协同选址问题。为了实现应急物资在不同级库间的协同储备管理与分散优化调拨,建立了应急物资储备级库选址的协同优化模型。用启发式算法通过MATLAB编程来实现,并用算例进行了验证。     

A beetle antennae search improved BP neural network model for predicting multi-factor-based gas explosion pressures

A gas explosion in an underground structure may cause serious damage to the human body and ground buildings and may result in huge economic losses. The pressure of the gas explosion is an important parameter in determining its severity and designating an emergency plan. However, existing empirical and computational fluid dynamics (CFD) methods for pressure prediction are either inaccurate or inefficient when considering multiple influencing factors and their interrelationships. Therefore, for a more efficient and reliable prediction, the present study developed a multifactorial prediction model based on a beetle antennae search (BAS) algorithm improved back propagation (BP) neural network. A total of 317 sets of data which considered factors of geometry, gas, obstacle, vent, and ignition were collected from previous studies. The results showed that the established model can predict pressures accurately by low RMSE (43.4542 and 50.7176) and MAPE (3.9666% and 4.9605%) values and high R² (0.7696 and 0.7388) values for training and testing datasets, respectively. Meanwhile, the BAS algorithm was applied to improve both the calculation efficiency and the accuracy of the proposed model by enabling a more intelligent hyperparameter tuning method. Furthermore, the permutation importance of input variables was investigated, and the length (L) and the ratio of length and diameter (L/D) of geometry were found to be the most critical factors that affect the explosion pressure level.     

基于遗传-蚁群算法的单层建筑火灾疏散路径规划研究

为解决如何快速、有效地找出单层建筑火灾最佳疏散路径问题,分析火灾发生时可能影响人员逃生及最佳路径生成的因素。提出基于遗传-蚁群算法求解火灾时期人员疏散路径。运用遗传算法的快速全局搜索能力,对蚁群算法初期所需要的信息素进行快速处理;利用蚁群算法的正反馈机制,快速找出可行路径,缩短搜索时间;并运用Matlab软件对其进行仿真。结果表明:遗传-蚁群算法所求得的路径生成质量有明显提高,并缩短路径生成时间,有助于提高人员逃生效率,降低人员伤亡率。     

1种基于CCRP火灾情况下的动态疏散算法

为在火灾发生时给逃生人群规划最优的安全疏散方案,基于CCRP算法开发1种火灾情况下受容量限制的动态疏散算法。该算法模型将发生火灾的建筑布局信息表达成节点连接弧形式,鉴于可预测危害蔓延对火灾逃生过程中疏散通道封锁时间的影响,为建筑网络图上的节点赋予“危险时刻”属性,逃生人员通过节点的时刻必须小于该节点的“危险时刻”才能规避火灾危险;基于eclipse软件平台,通过动态疏散算法与相似Dinesh算法应用实例对比仿真分析,验证基于CCRP的动态疏散算法的可行性与优越性。结果表明:通过该算法计算出的逃生路径保证逃生人员在最短时间疏散出去;且其具有安全性高和耗时少等优点。     

1种机器人工作区域协同搜索避障巡检策略

为解决工作区域搜索待操作目标的不确定性以及单一策略无法解决障碍物区域多搜索目标遍历与避障的问题,提出1种随机与固定搜索相结合的协同巡检策略。该策略在重点监测点通过引入非线性收敛因子及动态权重策略的改进灰狼优化算法遍历多任务点进行固定路线搜索;在非重点监测区域为差速转向移动机器人赋予三视野扫描线,并运用其灵活转动的特点进行随机路线搜索;通过交互式人工标记的方法定位搜索目标点并对其进行标记,运用改进灰狼优化算法对标记出的多目标点进行遍历顺序规划及D*算法避障到达;通过5个国际通用工程函数仿真测试改进的灰狼优化算法。结果表明:改进的灰狼优化算法能加快收敛速度,增强模型的求解精度,加强算法的稳定性,同时验证随机与固定相结合的区域协同搜索避障巡检策略的有效性。