Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

基于改进Hybrid A^(*)算法的阿克曼移动机器人路径规划

针对移动机器人路径规划的效率和所规划路径的安全性问题,基于阿克曼六轮转向模型,提出了一种基于改进Hybrid A^(*)算法的路径规划方法。通过改进Hybrid A^(*)算法中的启发式函数,引入距离惩罚函数,减少了节点搜索数量;通过构建安全走廊,引导移动机器人尽可能远离障碍物;在代价函数中加入了节点向前、换向和向后扩展的惩罚项,确保所规划路径的可执行性与安全性。通过仿真表明,基于改进Hybrid A^(*)算法的路径规划方法适用于阿克曼六轮移动机器人,提高了路径规划的效率,规划的路径更具安全保障。

Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method.Afterwards,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults adaptively.Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

Identification of Magnetic Bearing Stiffness and Damping Based on Hybrid Genetic Algorithm

Identifying the stiffness and damping of active magnetic bearings(AMBs)is necessary since those parameters can affect the stability and performance of the high-speed rotor AMBs system.A new identification method is proposed to identify the stiffness and damping coefficients of a rotor AMB system.This method combines the global optimization capability of the genetic algorithm(GA)and the local search ability of Nelder-Mead simplex method.The supporting parameters are obtained using the hybrid GA based on the experimental unbalance response calculated through the transfer matrix method.To verify the identified results,the experimental stiffness and damping coefficients are employed to simulate the unbalance responses for the rotor AMBs system using the finite element method.The close agreement between the simulation and experimental data indicates that the proposed identified algorithm can effectively identify the AMBs supporting parameters.

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data centre.Currently,web service composition gains maximum attention among researchers due to its significance in real-time applications.Quality of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS.But these models have failed to handle the uncertainties of QoS.The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users.On the other hand,trip planning is an essential technique in supporting digital map services.It aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the query.But the available web service composition solutions do not consider the complicated spatio-temporal features.For resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location awareness.The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,etc.At the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition plans.Followed by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite plans.Besides,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user rating.An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the “evolutionevaluation” iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the proposed method and associated algorithms.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

船舶混合动力系统能量管理预测控制方法研究

针对混合动力系统控制稳定性和节能减排问题,明确模型预测控制在船舶能量管理中的作用、研究现状及趋势。借助CiteSpace对船舶能量管理进行可视化分析,理清了船舶能量管理的发展脉络,并通过不同能量管理策略的对比分析揭示模型预测控制在船舶能量管理中的重要性;从预测建模、优化目标及约束、求解和改进策略等3个方面开展了船舶能量管理预测控制方法的分析;最后从考虑可再生能源的能量管理策略、建模与验证的标准化、协同优化和多维度评估等方面对船舶能量管理预测控制的未来研究进行展望。结果表明:模型预测控制在智能船舶、多能源船舶等复杂的动力系统的实时控制中具有重要潜力。与其他算法结合开展多目标算法融合是提升控制精度和计算实时性的重要途径,开展多维度测试评估有利于推动策略的实船应用。

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

船用承压结构变形场混合数字孪生监测模型方法实现

[目的]旨在为实现船舶的全生命健康监测设计一种面向结构健康监测的混合数字孪生系统。可实时采集及反馈关键舱室结构的变形,从而提升航运的信息化和安全管理能力。[方法]首先,采用奇异值分解法对多组载荷形成的物理场信息进行数据压缩降维得到特定的标准正交基,创建基向量与载荷关系的响应面模型,输出基于实时输入载荷的有限元降阶模型。其次,采用基于地统计学的克里金插值算法,按照特定拓扑结构布点,将实时的传感器数据和降阶模型输出的补充点位数据经由卡尔曼滤波算法进行融合修正,共同计算监测对象的变形情况。最后,通过构建变形监测软硬件系统,实现监测物理特性的采集到可视化的全过程。[结果]该系统在预设的载荷下,硬件采集系统能够稳定进行数据采集,配套的应用程序能够按照预期的要求进行实时可视化采集。[结论]该结构健康混合数字孪生系统满足船舶的健康监测需求,对未来船舶的高度一体化、智能化发展具有一定的参考意义。

考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型

随着交通出行需求的快速增长,我国高速公路网络面临的运输压力日渐增加,经常出现严重的交通拥堵现象。为了缓解高速公路交通拥堵,并实施更有针对性的交通管控措施,提出了考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型。首先,利用湖南省高速公路以及国道、省道的地理信息数据构建区域多层公路交通网络。然后,根据OD对间距离和OD交通量的差异,利用K-均值聚类算法对OD对进行聚类分析,将OD对划分为3个不同的类别。最后,应用遗传算法筛选出各类OD对中对拥堵贡献较大的出行群体,并建立有针对性的混合路径诱导模型,对拥堵贡献较大和拥堵贡献较小的出行群体分别应用不同的路径诱导方案。当OD需求扩样系数设置为6时,对OD对聚类可以将总出行成本进一步降低35186.03 min。在不进行OD对聚类时,使用规划路径的出行总数为79140,而实施OD对聚类后,使用规划路径的出行总数为70374。使用诱导路径的出行的平均出行时间由121.47 min下降为85.61 min,极少数出行(3.75%)的时间增加,且增加最大值低于3 min。对多个不同扩样系数进行敏感性分析进一步说明了考虑OD需求聚类的混合路径诱导模型具有良好的拥堵缓解效果。考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型可以用于识别对拥堵贡献较大的关键出行群体,进而制定有针对性的路径诱导策略,在缓解高速公路交通拥堵的同时能够减少对大多数出行者的影响,降低路径诱导策略的实施难度。另外,研究结果还表明:对出行距离较长的出行群体实施路径诱导能够更加有效地缓解区域多层公路交通网络中的交通拥堵。

考虑风电不确定性的电气综合能源系统混合尺度调控

为改善电-气互联综合能源系统中风电出力不确定性和多能传输差异对调控过程的影响,提出了基于改进小波融合算法的混合尺度调控方法.首先采用区间数学的方法,对系统中风电功率不确定性进行表示并给出风电处理策略.其次,考虑到不同能源传输特性的差异,提出了改进的小波融合算法,即先对电力网络中传感器信号数据进行多个不同小波基的多尺度分解,再对天然气系统信号数据中使用相同小波基分解的信号在混合尺度上实施加权数据融合,进行不同小波基的逆变换后得到融合信号.最后基于所搭建仿真模型,对比分析了不同调控方法的调控效果.结果表明本文所提方法的调控结果优于DMPC(分布式模型预测控制)滚动优化调控结果,且在改善了系统运行经济性的同时也提高了系统稳定性.

基于无线传播环境的无蜂窝大规模MIMO系统接入点部署优化

无蜂窝大规模多输入多输出(MIMO)系统通过在覆盖区域内部署大量的接入点(AP),可以为用户提供均匀、可靠的服务。传统的无蜂窝大规模MIMO系统采用随机部署,未考虑AP周围的路径损耗、阴影衰落散射物以及环境遮挡对覆盖质量的影响。为了考虑实际环境下无蜂窝大规模MIMO能实现均匀、一致的覆盖,提出了基于无线传播环境的AP部署方案。首先,通过混合概率路径损耗模型对无线传播环境进行表征,其次构建了以最大化平均吞吐量为目标的AP部署优化问题,最后将问题转化为马尔可夫博弈过程,并且基于多智能体深度确定性策略梯度(MADDPG)算法得出最优的AP部署策略。仿真结果表明,相比于传统的随机部署和现有AP部署策略,所提方案可明显改善复杂环境下的非均匀覆盖问题,为用户提供良好一致的均匀覆盖。