Crustal velocity structure of central Gansu Province from regional seismic waveform inversion using firework algorithm

The firework algorithm（FWA） is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model show that the FWA performs well in both solution quality and efficiency. We apply the FWA in this study to crustal velocity structure inversion using regional seismic waveform data of central Gansu on the northeastern margin of the Qinghai-Tibet plateau. Seismograms recorded from the moment magnitude（MW） 5.4 Minxian earthquake enable obtaining an average crustal velocity model for this region. We initially carried out a series of FWA robustness tests in regional waveform inversion at the same earthquake and station positions across the study region,inverting two velocity structure models, with and without a low-velocity crustal layer; the accuracy of our average inversion results and their standard deviations reveal the advantages of the FWA for the inversion of regional seismic waveforms. We applied the FWA across our study area using three component waveform data recorded by nine broadband permanent seismic stations with epicentral distances ranging between 146 and 437 km. These inversion results show that the average thickness of the crust in this region is 46.75 km, while thicknesses of the sedimentary layer, and the upper, middle, and lower crust are 3.15,15.69, 13.08, and 14.83 km, respectively. Results also show that the P-wave velocities of these layers and the upper mantle are 4.47, 6.07, 6.12, 6.87, and 8.18 km/s,respectively.

Improvement of the Firework Algorithm for Classification Problems

Attracted numerous analysts’consideration,classification is one of the primary issues in Machine learning.Numerous evolutionary algorithms(EAs)were utilized to improve their global search ability.In the previous years,many scientists have attempted to tackle this issue,yet regardless of the endeavors,there are still a few inadequacies.Based on solving the classification problem,this paper introduces a new optimization classification model,which can be applied to the majority of evolutionary computing(EC)techniques.Firework algorithm(FWA)is one of the EC methods,Although the Firework algorithm(FWA)is a proficient algorithm for solving complex optimization issue.The proficient of the FWA isn't fulfilled when being utilized for solving the classification issues.In this paper we previously proposed optimization classification model according to the classification issue.At that point we legitimately utilize the model with FWA to solve the classification issue.Finally,to investigate the performance of our model,we select 4 datasets in the experiments,and the results indicate that an improved FWA can upgrade the classification accuracy by using this model.

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'.

Quantum fireworks algorithm for optimal cooperation mechanism of energy harvesting cognitive radio

For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.

基于改进FWA的配电网优化重构方法研究

针对配电网重构问题,以降低网络损耗、提高节点电压为目标,建立了配电网重构模型,提出了一种解决配电网重构问题的改进烟花算法(firework algorithm,FWA)。算法使用环路编码压缩解空间,提高求解效率。基于核心烟花给出了火花爆炸幅度的计算方法,采用高斯和均匀分布均值混合的变异方式以及精英选择策略,提高解的多样性、搜索效率和收敛速度。为了验证所提算法的适用性和正确性,在IEEE33和IEEE69节点系统上进行了算法测试,结果表明,重构后的网络损耗明显降低,节点电压得到提高,所提算法具有较好的鲁棒性。将所提算法与其他算法进行比较,结果表明,所提算法在降低网络损耗、提高节点电压、求解效率等方面均优于其他算法。

配电网多目标优化重构模型及多目标烟花求解算法研究

针对配电网优化重构问题,以最小化有功网损、最小化电压偏移度以及最小化负荷平衡度作为目标函数建立配电网多目标优化重构模型。提出一种多策略混合的改进烟花算法(Improved fireworks algorithm,IMFWA)进行求解。算法采用不重复环路编码,压缩解空间,提高搜索效率;采用Sobol序列生成初始种群,增强种群的多样性和遍历性;利用优化烟花和随机烟花进行位移操作,保持种群多样性;采用高斯与柯西的混合变异方式,提高寻优效率;利用Pareto支配关系以及适应度与拥挤度函数组成的综合指标对最优解集进行排列选取,提高收敛速度。对IEEE-33节点系统进行仿真试验,验证了所提出的方法是有效和可行的,试验结果表明优化重构方案能够有效改善配电网的运行指标,并根据不同实际情况为电网人员提供重构方案。

基于烟花算法的反应釜温度预测控制

针对连续釜式反应过程由于大滞后造成的温度控制精度不高的问题,利用科学数据网格(scientific data grid, SDG)构建连续釜式反应过程温度控制系统结构图,在传统控制策略的基础上提出串级动态矩阵控制-比例积分微分(dynamicmatrixcontrol-proportion integrationdifferentiation,DMC-PID)温度预测控制策略。利用烟花算法完善DMC滚动优化环节,进而提高参数寻优的速度。搭建半实物仿真实验平台,通过OPC工具箱将DMC模块与可编程逻辑控制器(programmable logic controller, PLC)设备相连接进行实验验证。实验结果表明,基于烟花算法的DMC-PID串级系统,可以克服温度大滞后造成的影响,且DMC-PID串级控制对温度系统的控制精度和响应速度均有较大的提升。

一种基于计算智能的飞机部件装配序列优化方法

针对目前装配资源受限条件下大型部件装配规划存在效率低、性能差的问题,提出一种基于离散烟花算法的装配序列规划模型。建立了装配部件、装配资源和混合干涉矩阵,以描述不同情况下的装配干涉问题。为了加快可行解的求解速度,设计了包括爆炸幅度、烟花爆炸产生的火花数量和火花位置更新的策略。实验阶段,以机翼的装配过程为例对所提模型进行验证。结果表明,与离散粒子群优化算法相比,使用所提出的离散烟花算法获得的解的适应度函数值更优,所提算法适应度值提升约24.5%。

基于改进烟花优化算法的三维空间气体源定位

为探究三维空间中气体源定位及其源强反算问题,提出1种改进烟花爆炸算法(GWOFA)。将定位过程分为全局定位过程和局部定位过程。全局定位过程即结合灰狼优化算法和莱维飞行在三维空间中的全局搜索过程;局部定位过程是在全局定位的结果上的进一步开发过程,其通过引入边界条件的爆炸半径选取方式和选择策略更加高效地改进烟花优化算法实现。研究结果表明:本文算法相比于支持向量机回归模型(LinearSVR)、GWO算法和粒子群算法具有更高精确度,相比于GWO算法和粒子群算法具有更高稳定性和更低随机性;在气体源定位问题上,本文算法整体表现优于LinearSVR、GWO算法和粒子群算法。研究结果可为解决三维空间中气体源定位问题和相关参数估计问题提供新的思路方法。

基于离散烟花算法的通用弹药库存控制优化

近年来,随着我军实战化练兵水平的不断提高,对于后勤保障能力提升的要求更加迫切。但目前在通用弹药库存控制方面,难以满足目前高强度的训练需求,与实战化训练要求还有较大差距。论文针对目前存在的问题,构建三级库存控制模型,通过离散烟花算法进行求解,对于改善通用弹药的库存控制问题提出了新的思路。

基于表层温度深度学习的电缆接头绝缘劣化非接触式诊断

为提升电缆中间接头绝缘层劣化程度的现场诊断效率和准确度,提出一种基于表层温度自适应深度学习的接头绝缘劣化状态非接触式诊断方法。首先,对电缆接头及两端电缆的绝缘表层进行红外热成像,实现电缆接头中心两边多个对称区域的表层温度、接头两端电缆的表层温度的非接触式采集;其次,构建基于双隐层自编码极限学习机的深度学习网络,以挖掘表层温度数据内部深层次隐含特征,将提取的深度隐含特征作为随机森林诊断模型输入;然后,提出一种非线性动态自适应旋转角的量子旋转门以改进量子烟花算法的更新策略,并用于诊断模型参数优化;最后,结合接头表层红外温度和绝缘介质损耗角正切值构建数据集,对诊断模型进行训练和现场测试。实验结果表明,改进后的量子烟花算法可以较好地逼近全局最优解、收敛效率高,深度学习随机森林诊断模型具有较强的特征抽取和分类能力,参数优化后诊断模型的分类精度和稳定性得到有效提高,在小样本训练集条件下就能达到较好的诊断效果,可实现接头绝缘劣化状态的非接触式诊断。

改进烟花算法求解同时送取货选址路径问题

针对同时送取货的选址路径问题(Location-routing Problem with Simultaneous Pickup and Delivery,LRPSPD),设计一种改进烟花算法(Improved Firework Algorithm,IFWA)求解。首先,考虑仓库建设、车辆启用、车辆路径等成本因素,建立最小成本的LRPSPD模型,该模型强调需求点的送货需求和取货需求只能由一辆车同时进行服务。其次,设计一种改进烟花算法,该算法结合贪心聚类算法生成初始解,由烟花爆炸算子操作生成邻域解,利用变异操作协助产生新种群。最后,通过使用混合免疫算法、模拟退火算法求解相同算例,对结果进行分析比较,验证模型的可行性和改进算法的有效性。

基于烟花算法的智慧城市物联网数据流聚类方法

以往的智慧城市物联网数据流聚类方法对数据特征提取不精准,聚类速度慢。为了提高聚类速度,缩短数据流聚类的耗时,设计了基于烟花算法的智慧城市物联网数据流聚类方法。通过对样本矩阵的标准化计算,降低数据运算的压力,完成对物联网数据的预处理。在烟花算法的支持下,对数据进行去重处理,精准提取数据特征。再根据数据属性不同,计算数据的响应函数,构建数据聚类模型,利用余弦对数据进行聚类分析,实现物联网数据流的聚类。实验结果表明,该聚类方法的聚类平均耗时为15.52 ms,说明该方法能够有效缩短聚类耗时。

Research and Application of a Multi-Field Co-Simulation Data Extraction Method Based on Adaptive Infinitesimal Element

Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.

有色金属矿山横切割头掘进机械残余振动控制方法

为了提高掘进机械在运行过程中的稳定性,提出有色金属矿山横切割头掘进机械残余振动控制方法。首先对横切割头的载荷展开分析,发现横切割头的横摆速度会影响掘进机械的残余振动幅度,随着横摆速度的增大,振动幅度增加;然后建立最优横摆速度模型,引入混沌烟花算法获取残余振动幅度最小时的最优横摆速度;最后将最优横摆速度作为目标速度,根据横切割头的横摆速度计算速度偏差,采用模糊PID控制算法展开横摆速度控制,使实际横摆速度达到最优,以此实现横切割头掘进机械残余振动控制。实验结果表明,所提方法的载荷分析精度高、控制效果好、控制效率高。

基于IFWA⁃CS算法融合的移动机器人路径规划

为解决基本烟花算法(FWA)在移动机器人路径规划中存在收敛速度慢、易陷入局部最优等不足,首先将RRT算法与FWA算法结合以提升算法初始速度;其次,针对FWA算法存在收敛速度慢的问题,在其选择操作中采用精英选择与轮盘赌法相结合的选择机制增强算法的局部搜索能力;最后利用自适应莱维飞行策略改善其易陷入局部问题。仿真实验结果表明,融合算法的路径长度和收敛速度均得以提升,在移动机器人路径规划中具有很好的实用性。

基于大数据分析技术的企业管理成本智能化预测技术研究

市场经济环境下企业管理成本的智能化预测为企业快速、高效决策提供了数据支撑,有助于企业的健康发展。针对企业管理成本影响因素多、传统预测技术智能化水平低的问题,采用烟花算法对随机森林算法的参数进行优化,同时借助Informatica软件对影响企业管理成本的各种因素进行管理、集成,依次作为预测模型的输入。将构建的企业管理成本智能化系统应用于零售业企业和制造业企业中,分析了导致企业管理成本较高的原因,为企业发展决策的制定提供了一定的参考。

无线传感器网络中基于μ律爆炸算子的烟花虚拟力混合覆盖策略

针对烟花算法在无线传感器网络节点部署过程中易陷入局部最优导致节点分布不均匀、后期收敛速度慢等问题,本文提出一种基于μ律爆炸算子的烟花虚拟力混合算法(μFW–VFA).首先,采用μ律特性曲线重新定义爆炸算子,增强烟花间的差异性,通过动态调整μ值使烟花爆炸的数目和幅度随迭代次数动态调整,以平衡烟花局部和全局的寻优能力.其次,引入虚拟力调节停滞烟花内传感器节点的位置信息,加速烟花种群进化,增强算法跳出局部最优的能力,提高算法收敛速度.仿真实验表明,经μFW–VFA部署后,网络的重叠区域和监测盲区显著减少,有效提升了网络覆盖率并压缩节点移动距离.

采用离散烟花算法的移动群智感知异构任务分配

建立移动群智感知异构任务分配问题的数学模型,该模型考虑参与者的心理与行为过程,并引入环境信息和参与者健康状况、信誉度和测量时间等因素,通过寻找最优任务分配方案,最小化任务完成的总成本,该总成本包括补偿成本、数据损失成本和距离成本3方面。为求解该模型,提出一种引入了预测信息的离散烟花算法。该算法采用整数编码方式,利用模型中的距离和匹配度2种启发信息设计烟花爆炸算子,提出了爆炸振幅的分组线性预测策略和变异算子的自适应竞争机制。实验结果表明,与已有算法相比,所提算法在移动群智感知异构任务分配问题上能够搜索到更优的分配方案。

基于改进鲸鱼算法优化神经网络的飞机空气循环系统建模及故障分析

飞机空气循环系统在飞行过程中很容易出现故障,因此在地面上模拟仿真飞机空气循环系统有着重要意义.首先建立飞机空气循环系统仿真模型,针对该系统建立神经网络模型进行训练;引入PID控制算法调节参数以应对飞机遇到的不同飞行状况;针对神经网络模型引入鲸鱼算法,并运用烟花算法、重启机制和正余弦混沌双弦算法对鲸鱼算法进行改良.结果表明:模型可以模拟飞机引气到排出过程,以及不同情况下飞机空气循环系统4个主要组件的出口温度;神经网络系统可以根据输入指标数据预测不同组件的出口温度,并分辨出飞机空气循环系统是否出现故障及哪里出现故障.改进后的神经网络训练速度加快,准确率提升,明显降低了陷入局部最优值的可能性.