期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到27篇文章
< 1 2 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
1
作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
暂未订购 下载PDF
Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
2
作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
暂未订购 下载PDF
Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment 认领 引用
3
作者 LV Zhaoming PENG Rong 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2022年第3期240-254,共15页
Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent adv... Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods. 展开更多
关键词 ontology alignment multi-objective grasshopper optimization algorithm ε-dominance fast non-dominated sorting knowledge integration
暂未订购 下载PDF
Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM 认领 引用 被引量：14
4
作者 Jian Gao Litao Dai Wenjuan Zhang 《CES Transactions on Electrical Machines and Systems》 CSCD 2018年第1期160-165,共6页
For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnet... For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method. 展开更多
关键词 Improved Genetic Algorithm reduction of flux density spatial distortion sub-domain model multi-objective optimal design
暂未订购 下载PDF
Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm 认领 引用
5
作者 Xiaokan Wang Qiong Wang 《Journal on Internet of Things》 2021年第1期1-9,共9页
A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of op... A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect. 展开更多
关键词 Multi-objective improved genetic algorithm urban rail train train operation simulation multi particle optimization model
暂未订购 下载PDF
Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm 认领 引用 被引量：11
6
作者 Zesheng Wang Yanbiao Li +3 位作者 Kun Shuai Wentao Zhu Bo Chen Ke Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第1期70-84,共15页
Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-ob... Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators. 展开更多
关键词 Hybrid manipulator Bezier curve Improved optimization algorithm Trajectory planning Multi-objective optimization
暂未订购 下载PDF
Aerodynamic multi-objective integrated optimization based on principal component analysis 认领 引用 被引量：17
7
作者 Jiangtao HUANG Zhu ZHOU +2 位作者 Zhenghong GAO Miao ZHANG Lei YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第4期1336-1348,共13页
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,... Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem. 展开更多
关键词 Aerodynamic optimization Dimensional reduction Improved multi-objective particle swarm optimization（MOPSO） algorithm Multi-objective Principal component analysis
暂未订购 下载PDF
Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation 认领 引用
8
作者 Yan Wu Genqin Sun +4 位作者 Keming Su Liang Liu Huaijin Zhang Bingsheng Chen Mengshan Li 《Journal of Computer and Communications》 2017年第13期9-20,共12页
In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based o... In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems. 展开更多
关键词 Improved Particle Swarm Optimization Algorithm Double Populations Multi-Objective Adaptive Strategy Chaotic Sequence
暂未订购 下载PDF
An Improved ACO Path Planning Algorithm for Navigation in Weighed Lattice Map 认领 引用 被引量：1
9
作者 WANG Bofan MA Ziqing +2 位作者 SONG Zeyuan YAO Haizheng YUAN Quan 《同济大学学报（自然科学版）》 EI CAS CSCD 北大核心 2025年第S1期236-247,共12页
In autonomous navigation and robotics,particularly within intelligent transportation systems,efficient and precise path planning is essential for navigation through complex environments.While traditional path planning... In autonomous navigation and robotics,particularly within intelligent transportation systems,efficient and precise path planning is essential for navigation through complex environments.While traditional path planning algorithms such as ACO show potential,they frequently encounter limitations in directionality and local optima challenges.This paper introduces an enhanced algorithm—ACO-ESD.Through the implementation of a Step Direction Judgement mechanism that considers pheromone concentrations,heuristic functions,and supplementary indices,the ACOESD algorithm significantly improves path search directionality,expedites convergence,and effectively circumvents local optima.Simulation results indicate that the ACO-ESD algorithm surpasses traditional ACO algorithms in path efficiency,accuracy,and convergence rate,offering an effective solution for path planning in complex weighted lattice maps. 展开更多
关键词 path planning algorithm improved ant colony optimization weighted lattice map enhanced step direction mechanism multi-objective function elite ant selection strategy
暂未订购 下载PDF
基于IMOGOA优化的永磁同步电机新型滑模控制 认领 引用
10
作者 吕品 富金明 +2 位作者 王宁 商泽华 张晨熙 《黑龙江电力》 2026年第1期16-23,共8页
针对矿用永磁同步电机(PMSM)调速系统中基于传统指数趋近律的滑模控制方法中存在高超调量、较大转矩脉动及显著电流波动等问题,提出一种新型控制策略。提出一种新型趋近律,该趋近律在加快系统状态到达滑模面的同时,能有效抑制系统抖振,... 针对矿用永磁同步电机(PMSM)调速系统中基于传统指数趋近律的滑模控制方法中存在高超调量、较大转矩脉动及显著电流波动等问题,提出一种新型控制策略。提出一种新型趋近律,该趋近律在加快系统状态到达滑模面的同时,能有效抑制系统抖振,并依据此趋近律设计新型滑模速度控制器。为进一步提升系统性能,采取双矢量模型预测电流控制与新型滑模控制器相结合。通过拉丁超立方采样法提取电机运行的数据,建立以需要优化参数为输入、优化目标为输出的BP神经网络模型。在BP神经网络模型基础上,以电机a相电流波形畸变率、转速启动超调量以及稳态响应时间为优化目标,利用改进多目标蝗虫优化算法(IMOGOA)对滑模速度控制器参数进行优化。试验结果表明,与传统指数趋近律的滑模控制方法相比,新型控制策略在提升系统响应速度、降低转速超调量以及抑制系统抖振方面表现出明显的优势,有效改善了矿用PMSM调速系统的整体性能。 展开更多
关键词 永磁同步电机 滑模控制 新型趋近律 双矢量模型预测电流控制 多目标蝗虫优化算法
暂未订购 下载PDF
基于IGOA-LSTM的北斗卫星钟差预报 认领 引用
11
作者 孙鹏 廖威 +3 位作者 沈建军 熊辉 张倩斯 凌家龙 《微型电脑应用》 2026年第6期194-197,共4页
为了提升北斗卫星钟差预报结果的准确性,设计一种基于改进蝗虫优化算法(IGOA)—长短时记忆神经网络(LSTM)的北斗卫星钟差预报模型。采用Cubic混沌映射和调节系数非线性变化2种策略对GOA进行改进,利用改进的GOA搜索LSTM的最优参数,获得IG... 为了提升北斗卫星钟差预报结果的准确性,设计一种基于改进蝗虫优化算法(IGOA)—长短时记忆神经网络(LSTM)的北斗卫星钟差预报模型。采用Cubic混沌映射和调节系数非线性变化2种策略对GOA进行改进,利用改进的GOA搜索LSTM的最优参数,获得IGOA-LSTM模型。采用IGOA-LSTM模型对北斗卫星钟差进行预报,结果表明,IGOA-LSTM模型在卫星钟差预报方面的适用性较好,能够提升钟差预报精度和降低钟差预报结果的波动性。 展开更多
关键词 钟差预报 北斗卫星 改进蝗虫优化算法 长短时记忆神经网络
暂未订购 下载PDF
Optimal Site and Size of Distributed Generation Allocation in Radial Distribution Network Using Multi-objective Optimization 认领 引用 被引量：9
12
作者 Aamir Ali M.U.Keerio J.A.Laghari 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第2期404-415,共12页
Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an... Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an appropriate number of DGs and their capacity along with the best location.In this paper,the improved decomposition based evolutionary algorithm(I-DBEA)is used for the selection of optimal number,capacity and site of DG in order to minimize real power losses and voltage deviation,and to maximize the voltage stability index.The proposed I-DBEA technique has the ability to incorporate non-linear,nonconvex and mixed-integer variable problems and it is independent of local extrema trappings.In order to validate the effectiveness of the proposed technique,IEEE 33-bus,69-bus,and 119-bus standard radial distribution networks are considered.Furthermore,the choice of optimal number of DGs in the distribution system is also investigated.The simulation results of the proposed method are compared with the existing methods.The comparison shows that the proposed method has the ability to get the multi-objective optimization of different conflicting objective functions with global optimal values along with the smallest size of DG. 展开更多
关键词 Distribution system distributed generation multi-objective optimization active power loss improved decomposition based evolutionary algorithm(I-DBEA)
数据中心多能互补分布式能源系统设计与运行优化研究 认领 引用 被引量：10
13
作者 任效效 李小龙 +4 位作者 薛凯 吴炫 韩小渠 王进仕 严俊杰 《西安交通大学学报》 EI CAS CSCD 北大核心 2025年第1期93-104,共12页
为实现数据中心的低碳转型和能效提升,提出了一种集成多种可再生能源和储能设备的多能互补分布式能源系统作为其供能系统。建立了包含生命周期成本、碳排放、能耗、电网购电率和热量浪费率等指标的优化目标体系。制定了两种考虑负荷特... 为实现数据中心的低碳转型和能效提升,提出了一种集成多种可再生能源和储能设备的多能互补分布式能源系统作为其供能系统。建立了包含生命周期成本、碳排放、能耗、电网购电率和热量浪费率等指标的优化目标体系。制定了两种考虑负荷特征的运行策略,采用改进的多目标蝗虫优化算法对系统容量配置进行求解。为了分析优化目标和运行策略对系统优化的影响,通过组合不同优化目标和运行策略形成了多种优化设计方案,采用层次分析法和熵权法确定评价指标的权重,并利用多准则妥协解排序法进行评价排序。针对青海省某数据中心的案例,获得其能源系统的优化设计与运行方案。研究表明:运行策略主要影响吸收式制冷机和地源热泵的容量,而优化目标主要影响储能设备的容量;在相同的运行策略下,增加优化目标可提升系统的综合性能;对于不同的方案,生命周期成本、碳排放、能耗等基础优化目标值变化较小,而电网购电率和热量浪费率变化较大;通过实施五目标优化,即在基础优化目标的基础上增加热量浪费率、电网购电率,系统的综合性能得到了显著提升,其中热量浪费率和电网购电率分别降低了62.30%和25.92%,可再生能源发电占比增加了2%。 展开更多
关键词 数据中心 多能互补分布式能源系统 优化设计 运行策略 多目标蝗虫优化算法
暂未订购 下载PDF
基于改进多目标蝗虫算法的压缩机叶轮参数优化研究 认领 引用 被引量：2
14
作者 任云鹏 李臻志 +4 位作者 宋方 李安帅 杨强辉 刘佳豪 邵佳康 《机电工程》 CAS 北大核心 2025年第5期856-865,共10页
针对常用基本算法在模型复杂的叶轮部件优化设计上寻优效果不佳等问题,提出了一种融合柯西变异和反向学习的改进多目标蝗虫优化算法(COMOGOA),对离心压缩机叶轮进行了参数优化设计。首先,分析了基本蝗虫优化算法(GOA)及多目标优化问题原... 针对常用基本算法在模型复杂的叶轮部件优化设计上寻优效果不佳等问题,提出了一种融合柯西变异和反向学习的改进多目标蝗虫优化算法(COMOGOA),对离心压缩机叶轮进行了参数优化设计。首先,分析了基本蝗虫优化算法(GOA)及多目标优化问题原理,对多目标蝗虫优化算法(MOGOA)进行了改进,融合了柯西变异和反向学习改进策略,并利用测试函数与常用优化算法对比验证了其性能;然后,以离心压缩机叶轮部件为研究对象,对其建立了理论数值模型,利用改进的COMOGOA对模型设计参数进行了寻优,并与其他优化算法进行了对比分析;最后,在考虑了数值仿真、模型假设等因素带来的误差影响情况下,利用ANSYS-CFX数值验证了仿真分析,结合叶轮气动特性及原因进一步验证了优化效果。研究结果表明:优化叶轮后,设计工况下的压缩比显著提升了4.370%,等熵效率增强了1.529%,叶轮得到了改善,从而提升了压缩机的整体性能。COMOGOA算法在叶轮部件复杂模型的优化设计中有着更为出色的寻优效果,也为此类复杂部件优化设计提供了合理参考,具有一定的工程应用价值。 展开更多
关键词 离心式压缩机 参数优化算法 融合柯西变异和反向学习的改进多目标蝗虫优化算法 ANSYS-CFX Cubic混沌模型 随机权重策略
暂未订购 下载PDF
基于动态特征参数和改进GOA-BP神经网络的车用PEMFC退化趋势预测 认领 引用
15
作者 薛发俊 杨继斌 +4 位作者 邓鹏毅 武小花 陈丽 王文龙 胡怀祥 《汽车工程》 EI CSCD 北大核心 2025年第11期2150-2158,2167,共9页
针对质子交换膜燃料电池(PEMFC)剩余使用寿命(RUL)预测中动态工况表征不足及传统优化算法易陷入局部最优的问题,提出一种融合动态特征参数及改进蚱蜢优化算法(IGOA)与BP神经网络相结合的预测方法。首先,通过季节趋势分解方法提取电压数... 针对质子交换膜燃料电池(PEMFC)剩余使用寿命(RUL)预测中动态工况表征不足及传统优化算法易陷入局部最优的问题,提出一种融合动态特征参数及改进蚱蜢优化算法(IGOA)与BP神经网络相结合的预测方法。首先,通过季节趋势分解方法提取电压数据的季节性分量,同时量化工况周期内的功率波动率,并采用灰色关联度分析筛选关键特征参数。然后,利用IGOA优化BP神经网络的超参数组,构建IGOA-BP神经网络预测模型。最后,基于实车数据和实验室测试数据集验证了模型性能。结果表明,提出的方法具有更高的预测精度,平均绝对百分比误差小于0.06%,能够实现更精确的燃料电池RUL预测。 展开更多
关键词 燃料电池 寿命预测 动态特征参数 改进蚱蜢优化算法 BP神经网络
暂未订购 下载PDF
基于层次加权排列熵与IGOA-BiLSTM的高压共轨喷油器剩余寿命预测方法 认领 引用
16
作者 屈蓓佳 高先理 +3 位作者 贾宝富 柯赟 姚崇 宋恩哲 《计算机集成制造系统》 EI CSCD 北大核心 2025年第8期3046-3056,共11页
针对预测模型参数选取不当而导致高压共轨喷油器剩余寿命预测精度较低的问题,提出一种基于层次加权排列熵(HWPE)与改进蝗虫优化算法(IGOA)优化双向长短时记忆网络(BiLSTM)的高压共轨喷油器剩余寿命预测方法。首先,由于HWPE能够充分考虑... 针对预测模型参数选取不当而导致高压共轨喷油器剩余寿命预测精度较低的问题,提出一种基于层次加权排列熵(HWPE)与改进蝗虫优化算法(IGOA)优化双向长短时记忆网络(BiLSTM)的高压共轨喷油器剩余寿命预测方法。首先,由于HWPE能够充分考虑时间序列高频和低频信息,提取全寿命周期数据的HWPE构建健康度指标(HI);然后,针对BiLSTM最优参数选取困难的问题,提出了一种改进蝗虫优化算法,通过引入混沌策略以丰富初始种群的多样性和随机性,重构线性因子增强全局搜索和局部开发的能力,并引入迁徙策略提高位置更新的质量,自适应地获取BiLSTM最优参数组合;最后,将HWPE与健康度指标分别作为IGOA-BiLSTM模型的输入输出进行训练测试,将输出的HI拟合寿命退化曲线并预测失效点,实现喷油器的剩余寿命预测。通过与其它常用方法对比分析,IGOA在参数寻优方面性能更好,所提出剩余寿命预测方法具有更高的预测精度。 展开更多
关键词 剩余寿命预测 层次加权排列熵 改进蝗虫优化算法 双向长短时记忆网络 高压共轨喷油器
暂未订购 下载PDF
基于改进蝗虫算法优化长短时记忆神经网络的多参数瓦斯浓度预测模型研究 认领 引用 被引量：22
17
作者 王雨虹 王淑月 +1 位作者 王志中 任日昕 《传感技术学报》 CAS CSCD 北大核心 2021年第9期1196-1203,共8页
为了高效挖掘煤矿安全监测监控系统海量数据中包含的有效信息,提高煤矿瓦斯浓度预测精度,提出一种改进的蝗虫优化算法(IGOA)优化长短时记忆神经网络(LSTM)的多参数瓦斯浓度预测模型。首先对瓦斯多参数时间序列进行相关性分析和小波去噪... 为了高效挖掘煤矿安全监测监控系统海量数据中包含的有效信息,提高煤矿瓦斯浓度预测精度,提出一种改进的蝗虫优化算法(IGOA)优化长短时记忆神经网络(LSTM)的多参数瓦斯浓度预测模型。首先对瓦斯多参数时间序列进行相关性分析和小波去噪;其次通过重构线性缩减因子c、引入柯西-高斯混合变异和最优邻域扰动策略联合改进蝗虫优化算法,提高其全局寻优能力,以此来优化LSTM相关超参数,构建瓦斯浓度预测模型;最后,以实测数据为样本进行实验验证,将提出的模型与BP、LSTM、PSO-LSTM以及GOA-LSTM模型对比,可得到提出的模型具有更好的预测效果,平均绝对百分比误差和均方根误差两种误差评价指标分别为0.531%、2.48×10-3。结果表明,提出的瓦斯浓度预测模型具有更高的预测性能。 展开更多
关键词 瓦斯浓度预测 改进蝗虫优化算法 LSTM 多参数时间序列 深度学习
暂未订购 下载PDF
基于改进蝗虫优化算法的光伏电池模型参数辨识 认领 引用 被引量：13
18
作者 吴忠强 申丹丹 +1 位作者 尚梦瑶 戚松崎 《计量学报》 CSCD 北大核心 2020年第12期1536-1543,共8页
针对蝗虫优化算法容易陷入局部最优、收敛精度不足等缺点,提出一种改进蝗虫优化算法。将混沌算法与蝗虫优化算法融合,对蝗虫优化算法进行混沌初始化,改善初始种群质量;再引入差分进化算法的差分策略,通过变异、交叉和选择过程,维持种群... 针对蝗虫优化算法容易陷入局部最优、收敛精度不足等缺点,提出一种改进蝗虫优化算法。将混沌算法与蝗虫优化算法融合,对蝗虫优化算法进行混沌初始化,改善初始种群质量;再引入差分进化算法的差分策略,通过变异、交叉和选择过程,维持种群的多样性,增大算法跳出局部最优的可能性,从而使算法能搜索到更好的解;在个体更新部分引入了粒子群算法的思想,以当前的最优个体为目标进行个体位置更新,加快算法寻优速度。将改进蝗虫优化算法用于多晶硅太阳能电池模型参数的辨识中,并通过与其它智能优化算法的比较,验证了改进蝗虫算法辨识太阳能电池参数的有效性和优越性。通过实验验证了改进蝗虫优化算法在不同光照下对太阳能电池参数的辨识效果。 展开更多
关键词 计量学 光伏电池 参数辨识 改进蝗虫优化算法 差分进化算法 混沌初始化
暂未订购 下载PDF
三维荧光光谱结合IGOA-SVM分类鉴别油类污染物 认领 引用 被引量：7
19
作者 程朋飞 朱燕萍 +2 位作者 潘金燕 崔传金 张怡 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2024年第4期1031-1038,共8页
溢油污染是一种典型的环境污染形式,通过多重渠道危害着生物多样性和人类自身安全。因此,针对油类污染物自身组成成分及其特性,采用多种方法相结合的方式,对其进行实时、精确、高效的检测对生态环境监测具有重要意义。三维荧光光谱分析... 溢油污染是一种典型的环境污染形式,通过多重渠道危害着生物多样性和人类自身安全。因此,针对油类污染物自身组成成分及其特性,采用多种方法相结合的方式,对其进行实时、精确、高效的检测对生态环境监测具有重要意义。三维荧光光谱分析法以其检测精度高、实时性好、操作简便、干扰性小等优势在荧光类物质检测领域应用十分广泛。三维荧光光谱结合支持向量机等算法在物质分类鉴别和浓度预测方面取得较好的成效,但仍存在收敛速度慢、易陷入局部最优等缺陷。将三维荧光光谱与改进蚱蜢优化支持向量机算法(IGOA-SVM)相结合,提出一种对油类污染物分类鉴别的新方法。首先,以0.1 mol·L-1十二烷基硫酸钠溶液作为溶剂,将0#柴油、95#汽油和煤油以不同浓度配比配制成0#柴油和95#汽油、0#柴油和煤油两组分混合样本20个和18个,三组分混合样本20个,各取一半为训练集和测试集。然后,采用F-7000荧光光谱仪采集混合溶液的荧光数据,分析三种油的标准溶液及配制的混合溶液,发现荧光光谱均在一定范围内存在不同程度的重叠现象,仅利用光谱检测无法准确鉴别。最后,结合混沌初始化、精英优化算法和差分进化算法对蚱蜢优化算法进行改进,提取激发波长270 nm,发射波长270~450 nm范围内的荧光峰数据作为训练的输入值,以三种分类标签作为输出,将数据分别输入到蚱蜢优化支持向量机算法(GOA-SVM)、粒子群优化支持向量机算法(PSO-SVM)和遗传优化支持向量机算法(GA-SVM)中进行训练,IGOA-SVM模型在收敛速度、稳定性和跳出局部最优能力上明显优于GOA-SVM、PSO-SVM和GA-SVM,为油类污染物的准确鉴别提供了一种新思路。 展开更多
关键词 三维荧光光谱 改进蚱蜢优化算法 支持向量机 石油类污染物
暂未订购 下载PDF
基于IGOA-ELM的拱桥多节段吊装扣挂施工线形预测方法 认领 引用 被引量：8
20
作者 廖宇芳 刘斌 +2 位作者 于孟生 王希瑞 彭曦 《公路交通科技》 CAS CSCD 北大核心 2022年第11期95-105,共11页
为了实现对大跨度钢管混凝土拱桥扣索索力的高效率优化,提出了一种基于改进的蝗虫优化算法(IGOA)和极限学习机(ELM)的线形预测方法和索力优化模型。采用非线性递减函数和精英保留策略改进蝗虫优化算法,使其适应高维优化问题。建立了基... 为了实现对大跨度钢管混凝土拱桥扣索索力的高效率优化,提出了一种基于改进的蝗虫优化算法(IGOA)和极限学习机(ELM)的线形预测方法和索力优化模型。采用非线性递减函数和精英保留策略改进蝗虫优化算法,使其适应高维优化问题。建立了基于改进蝗虫算法优化极限学习机的拱桥线形预测模型,将有限元模型的计算结果作为极限学习机的训练样本并记忆最佳训练参数,建立了基于IGOA-ELM算法的索力优化模型。以某大跨度钢管混凝土拱桥为工程背景进行了应用研究,根据实测线形结果对优化模型结果进行了验证。结果表明:IGOA优化后的极限学习机相较标准ELM、BP神经网络和SVM模型预测精度更高;优化输入权值矩阵和隐含层偏差后的ELM模型可以精确预测不同索力组合下的成拱线形,对数据样本的泛化能力较强,对于样本索力组合的预测结果十分接近有限元模型的实际计算结果;IGOA算法对高维优化问题具有较好的收敛性,与标准PSO、标准GOA算法相比,IGOA算法均能收敛至不同测试函数的理论最优值附近,算法性能得到大幅提升;基于IGOA-ELM算法优化得到的扣索张拉力相较设计张拉力有效改善了成拱线形,达到了一次斜拉扣挂下的线形优化目标;与基于传统有限元法进行优化的方式相比,IGOA-ELM优化耗时最短,有效提高了结构优化效率。 展开更多
关键词 桥梁工程 线形预测 极限学习机 钢管混凝土拱桥 改进的蝗虫优化算法 非线性收敛
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈