Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

Classification for Glass Bottles Based on Improved Selective Search Algorithm

The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in specifications and models.This paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of classifiers.In the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is proposed.An integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by SS.In the classification stage,the improved SS algorithm is used to extract target regions.The target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained classifier.Experiments demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise.

Improved hyper-spherical search algorithm for voltage total harmonic distortion minimization in 27-level inverter

Multi-level inverters(MLIs)have become popular in different applications such as industrial power control systems and distributed generations.There are different forms of MLIs.The cascaded MLIs(CMLIs)have some special advantages among them such as more different output voltage levels using the same number of components and higher power quality.In this paper,a 27-level inverter switching algorithm considering total harmonic distortion(THD)minimization is investigated.Switching angles of the inverter switches are achieved by minimizing a THD-based objective function.In order to minimize the THD-based objective function,the hyper-spherical search(HSS)algorithm,as a novel optimization algorithm,is improved and the results of improved HSS(IHSS)are compared with HSS algorithm and other five evolutionary algorithms to show the advantages of IHSS algorithm.

Improved Interleaved Single-Ended Primary Inductor-Converter forSingle-Phase Grid-Connected System

The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power.An improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable source.This proposed converter minimizes ripples,reduces electromagnetic interference due tofilter elements and the contin-uous input current improves the power output of PV panel.A Crow Search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller.The optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise time.This voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI controller.The response of the proposed converter is thus improved with less input current.After implementing CSA based PI the efficiency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.

Deep kernel extreme learning machine classifier based on the improved sparrow search algorithm

In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.

Object Recognition Algorithm Based on an Improved Convolutional Neural Network

In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.

Symmetric Workpiece Localization Algorithms: Convergence and Improvements

Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.

Optimised trajectory tracking control for quadrotors based on an improved beetle antennae search algorithm

This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references quickly. At first, nonsingular fast terminal slidingmode control is developed, which can guarantee not only the stability but also finite-timeconvergence of the closed-loop system. As the parameters of the designed controllers playa vital role for control performance, an improved beetle antennae search algorithm is proposedto optimise them. By employing the historical information of the beetle’s antennaeand dynamically updating the step size as well as the range of its searching, the optimisingis accelerated considerably to ensure the efficiency of the quadrotor control. The superiorityof the proposed control scheme is demonstrated by simulation experiments, from whichone can see that both the error and the overshooting of the trajectory tracking are reducedeffectively.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.

基于VMD-ISSA-GRU组合模型的短期风电功率预测

为解决风速不确定性和波动性造成风电功率预测精度不高的问题,提出一种基于变分模态分解(VMD)、改进麻雀搜索算法(ISSA)和门控循环神经网络(GRU)的VMD-ISSA-GRU组合模型。首先,利用中心频率法确定采用VMD分解后的模态分量个数,这样有效避免了过分解或者分解不充分。其次引入混沌映射、非线性递减权重以及一个突变策略来改进麻雀搜索算法,用于优化门控循环神经网络,然后对分解得到的各个子序列建立ISSA-GRU预测模型,最后叠加每个子序列的预测值得到最终的预测值。将该模型用于实际风电功率预测,实验结果表明:VMD-ISSA-GRU组合模型的平均绝对误差、平均绝对百分比误差、均方根误差分别为1.2118MW、1.8900及1.5916MW;相较于传统的GRU、长短时记忆(LSTM)神经网络、BiLSTM(Bi-directional LSTM)神经网络模型以及其他组合模型在预测精度上都有明显的提升,能很好地解决风电功率预测精度不高的问题.

基于改进引力搜索算法的水轮机调节系统仿真

针对现阶段水电机组存在多种复杂工况、工程计算受限于算法本身的复杂性等问题,提出一种改进的引力搜索算法(改进PSOGSA),以此提高水轮机控制参数的优化性能,弥补传统控制策略难以满足动态需求的不足.首先,结合PSO算法,在GSA的速度更新公式中引入学习因子进行改进.其次,应用一种权重系数优化其位置更新公式,提高算法的自适应性.最后,结合相关仿真建模试验,使用所提改进PSOGSA对水轮机调节系统PID参数进行优化调节.仿真结果表明,在5%空载频率扰动下,改进PSOGSA的PID控制器明显优于上述传统算法,所调节的模型系统能在更短时间内趋于稳定,此时的超调量远低于传统算法,表明此改进PSOGSA在后续迭代中具备更高的迭代效率,并且改善了常规算法中易陷入局部最优的问题,从而证明了改进PSOGSA的合理有效性,水轮机调节系统的控制效果在一定程度上得到优化.

基于CNN-GRU-ISSA-XGBoost的短期光伏功率预测

针对光伏功率随机性及波动性大,单一预测模型往往难以准确分析历史数据波动规律,从而导致预测精度不高的问题,提出一种基于卷积神经网络-门控循环单元(CNN-GRU)和改进麻雀搜索算法(ISSA)优化的极限梯度提升(XGBoost)模型的短期光伏功率预测组合模型.首先去除历史数据中的异常值并对其进行归一化处理,利用主成分分析法(PCA)进行特征选取,以便更好地识别影响光伏功率的关键因素.然后采用CNN网络提取数据的空间特征,再经过GRU网络提取时间特征,针对XGBoost模型手动配置参数困难、随机性大的问题,利用ISSA对模型超参数寻优.最后对两种方法预测的结果用误差倒数法减小误差的同时对权重进行更新,得到新的预测值,从而完成对光伏功率的预测.实验结果表明,所提出的CNN-GRU-ISSA-XGBoost组合模型具有更强的适应性和更高的精度.

基于特征选择及ISSA-CNN-BiGRU的短期风功率预测

针对风电功率随机性大、平稳性低,以及直接输入预测模型往往难以取得较高精度等问题,提出了一种基于特征选择及改进麻雀搜索算法(ISSA)优化卷积神经网络-双向门控循环单元(CNN-BiGRU)的短期风电功率预测方法。首先,利用变分模态分解(VMD)将原始功率分解为一组包含不同信息的子分量,以降低原始功率序列的非平稳性,提升可预测性,同时通过观察中心频率方式确定模态分解数。其次,对每一分量采用随机森林(RF)特征重要度的方法进行特征选择,从风速、风向、温度、空气密度等气象特征因素中,选取对各个分量预测贡献度较高的影响因素组成输入特征向量。然后,建立各分量的CNN-BiGRU预测模型,针对神经网络算法参数难调、手动配置参数随机性大的问题,利用ISSA对模型超参数寻优,自适应搜寻最优参数组合。最后,叠加各分量的预测值,得到最终的预测结果。以中国内蒙古某风电场实际数据进行仿真实验,与多种单一及组合预测方法进行对比,结果表明,本文所提方法相比于其他方法具有更高的预测精度,其平均绝对百分比误差值达到2.644 0%;在其他4个数据集上进行的模型准确性及泛化性验证结果显示,模型平均绝对百分比误差值分别为4.385 3%、3.174 9%、1.576 1%和1.358 8%,均保持在5.000 0%以内,证明本文所提方法具有较好的预测精度及泛化能力。

考虑碳排放的分布式电源优化配置

对分布式电源接入配电网进行合理的优化配置,能在兼顾运营商和用户利益的同时,改善系统整体电压分布。建立了综合考虑分布式电源投资成本、用户购电成本、网损费用和碳排放费用的多目标优化模型。利用改进层次分析法确定各目标的权重,进而转化为单目标函数规划问题。针对天牛须算法个体单一性在解决高维复杂问题时精度低,优化效果不佳的问题,提出了一种改进天牛须粒子群算法,利用混沌映射对参数进行调整,引入动态惯性权重、莱维飞行机制,提高了收敛速度。以IEEE33节点系统为例,将改进天牛须粒子群算法与粒子群算法及天牛须粒子群算法的效果对比,验证改进算法对分布式电源优化配置问题的可行性,有效降低了碳排放费用、用户购电费用,减少了系统网损,改善了系统整体电压分布。

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

跳跃跟踪SSA交叉迭代AP聚类算法

针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入者位置更新不足的问题,设计了一种跳跃跟踪优化策略,通过考虑偏好阻尼因子的跳跃策略设计大步长更新发现者,增加麻雀搜索算法的全局勘探能力和寻优速度,加入者设计动态小步长跟踪领头雀更新位置,同时,利用自适应种群划分机制更新发现者和加入者的比重,增加算法的后期局部开发能力和寻优速度;其次,设计基于扰动因子的Tent映射,在此基础上增加3个参数,使映射分布范围增大,并避免了陷入小周期点和不稳周期点;最后,引入轮廓系数作为评价函数,跳跃跟踪麻雀搜索算法自动寻找较优的p和λ,代替手动输入参数,并融合基于扰动因子的Tent映射优化近邻传播算法,交叉迭代确定最优簇数.使用多种算法聚类University of California Irvine数据集的10种公共数据集,仿真结果表明,本文提出的聚类算法与经典近邻传播算法、基于差分改进的仿射传播聚类算法、基于麻雀搜索算法优化的近邻传播聚类算法和进化近邻传播算法相比具有更优的搜索效率以及聚类精度.对国家信息数据进行了聚类分析,提出的方法更加准确有效合理,具有较好的应用价值.

一种多无人机协同优先覆盖搜索算法

针对应急救援行动中存在的受灾区域大、重点区域分布不均匀、救援时间有限等问题,提出一种多UAV协同区域优先覆盖搜索算法。对搜索区域进行离散栅格化处理,根据灾情预估信息对搜索区域中的每个网格进行概率标记;通过K-means++聚类算法将搜索区域划分成大小相似、个数与UAV数量相等的子区域,依据聚类中心确定每个子区域的搜索起点,使多架UAV分区协同搜索整个区域;根据网格概率和当前距离之间的平衡关系计算出每个网格的分数,改进贪心算法,以此分数为基准在子区域中进行优先搜索和减少重复路径,引入A^(*)算法解决网格分数冗余问题。仿真结果表明:所提算法在保证优先搜索的同时缩短了路径长度和搜索时间,为应急救援中的搜索难题提供了一种有效的解决办法。

大坝渗压混合预测的STL分解-集成学习模型

针对目前大坝渗压预测研究大多未区分影响因素对渗压不同特征成分贡献的差异,降低了模型的可解释性,且现有的预测模型大多采用单一算法,存在难以区分具有高度非线性和非稳态混合特征的渗流压力序列模式等问题,本文提出一种基于STL分解和集成学习策略的渗压可解释混合预测模型。该模型首先通过时间序列分解(STL)将原始渗压时间序列分解为季节项、趋势项和余项,以避免现有模型在渗流压力预测中模式混淆的不足;然后,不同成分的变化特征可采用多策略改进麻雀搜索算法(MSISSA)优化的核极限学习机(KELM)和卷积神经网络组合门控递归单元(CNN-GRU)组成的集成学习模型来识别;此外,还采用单次单因子法(OFAT)分析影响因素对渗流压力不同特征成分的贡献,从而改变输入因素的权重,以提高模型的可解释性。案例分析结果表明,在确保模型可解释性的同时,所提出的混合模型与基于单一算法的模型相比,预测精度平均提高了48.44%;与其他集成预测模型相比,预测精度平均提高了11.42%,验证了所提模型的有效性,为大坝渗流安全监控提供了新的建模方法。