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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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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
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Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM 认领 引用 被引量：14
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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
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Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm 认领 引用
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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
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Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm 认领 引用 被引量：8
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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
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Aerodynamic multi-objective integrated optimization based on principal component analysis 认领 引用 被引量：16
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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
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Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation 认领 引用
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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
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基于CLSRIME-XGBOOST的带式输送机托辊故障诊断方法 认领 引用 被引量：5
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作者 江帆 程舒曼 +4 位作者 朱真才 周公博 李强 刘全辉 宋鸿炎 《振动．测试与诊断》 EI CSCD 北大核心 2025年第4期666-673,840,共8页
针对声音信号分析在诊断带式输送机托辊故障中的高维特征存在信息冗余、计算量大和诊断效果不理想等问题,笔者构建了声音信号特征精简策略,基于Circle混沌映射、Levy飞行策略和正弦因子改进了霜冰优化算法(rime optimization algorithm... 针对声音信号分析在诊断带式输送机托辊故障中的高维特征存在信息冗余、计算量大和诊断效果不理想等问题,笔者构建了声音信号特征精简策略,基于Circle混沌映射、Levy飞行策略和正弦因子改进了霜冰优化算法(rime optimization algorithm,简称RIME),记作CLSRIME。再结合极致梯度提升模型(extreme gradient boosting,简称XGBOOST),构建了CLSRIME-XGBOOST带式输送机托辊轴承故障诊断方法。首先,利用梅尔倒谱系数(Melscale frequency cepstral coefficient,简称MFCC)融合方法提取信号关键特征,并通过t-分布领域嵌入算法(t-distributed stochastic neighbor embedding,简称tSNE)进行降维,构建了基于MFCC和tSNE的精简特征提取策略;其次,针对RIME存在初始种群分布不均、霜冰粒子搜索能力弱、收敛速度较慢的问题,引入Circle混沌映射、Levy飞行策略和正弦因子,设计了CLSRIME;最后,利用CLSRIME优化XGBOOST中树的深度、迭代次数及学习率等参数,构建了基于CLSRIME-XGBOOST的诊断模型。结果表明,所提方法能够精简表征托辊轴承故障状态的特性信息,改善了RIME的优化性能,提高了传统XGBOOST诊断模型的准确率,为带式输送机托辊故障诊断提供了新思路。 展开更多
关键词 带式输送机 改进RIME算法 MFCC XGBOOST 故障诊断
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基于MIC特征提取与ICEEMD-RIME-DHKELM的建筑业碳排放预测模型 认领 引用 被引量：6
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作者 张新生 聂达文 陈章政 《环境工程》 CAS CSCD 2025年第4期46-58,共13页
为解决建筑业碳排放研究中影响因素选取局限性、数据预处理不足、碳排放复杂动态变化及非线性问题,提出了一种基于最大信息系数(MIC)特征提取、改进互补集合经验模态分解(ICEEMD)、雾凇优化算法(RIME)与深度混合核极限学习机(DHKELM)的... 为解决建筑业碳排放研究中影响因素选取局限性、数据预处理不足、碳排放复杂动态变化及非线性问题,提出了一种基于最大信息系数(MIC)特征提取、改进互补集合经验模态分解(ICEEMD)、雾凇优化算法(RIME)与深度混合核极限学习机(DHKELM)的建筑业碳排放量预测模型。首先,根据IPCC计算方法,从直接和间接两个方面测算1992—2021年我国建筑业碳排放量,基于STIRPAT模型选取年末总人口数、国内生产总值、建筑业房屋竣工面积和能源结构等17个影响建筑业碳排放量的因素,然后利用灰色关联分析和MIC方法两阶段筛选出12个关键影响因素;其次,使用ICEEMD将建筑业碳排放量分解为多个平稳序列和一个残差项,并将其分别代入RIME算法优化关键参数后的DHKELM模型中。最后,将各分解序列的预测结果相加获得建筑业碳排放预测值,并对比分析多种基准模型的预测结果。结果显示:MIC-ICEEMD-RIME-DHKELM模型的预测性能最优,其均方根误差、平均绝对误差、平均绝对百分比误差和绝对相关系数分别为0.2782亿t、0.2672亿t、1.3783%和0.9576,均优于其他模型,证明该模型适用于建筑业碳排放量的预测。该研究成果为建筑业的低碳发展提供理论支持和技术参考。 展开更多
关键词 建筑业 碳排放 最大信息系数 改进互补集合经验模态分解 雾凇优化算法 深度混合核极限学习机
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基于IRIME-BP-LSTM模型的继电保护装置剩余寿命预测方法 认领 引用 被引量：2
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作者 张洪嘉 戴志辉 +1 位作者 贺欲飞 贾文超 《电力系统保护与控制》 EI CSCD 北大核心 2025年第15期125-134,共10页
目前继电保护装置寿命预测理论中存在缺少对单个装置状态准确评估预测、预测数据无法根据实际运行情况及时修正等问题,导致预测结果不可靠。对此,提出基于改进霜冰优化算法(improved rime optimization algorithm,IRIME)优化反向传播(ba... 目前继电保护装置寿命预测理论中存在缺少对单个装置状态准确评估预测、预测数据无法根据实际运行情况及时修正等问题,导致预测结果不可靠。对此,提出基于改进霜冰优化算法(improved rime optimization algorithm,IRIME)优化反向传播(backpropagation,BP)神经网络与长短期记忆网络(long short memory network,LSTM)模型的继电保护装置剩余寿命预测方法。首先,总结运维经验与规程要求,建立保护装置状态评估指标集,形成初始输入向量。其次,引入柯西变异机制改进霜冰优化算法,利用IRIME对BP神经网络初始参数进行优化。然后,将初始输入向量赋予优化后的神经网络,进行保护装置状态评估,形成装置运行状态的表征向量并构建时间序列。最后,将构建的时间序列输入到LSTM网络中进行保护装置剩余寿命的预测。案例验证结果表明,该方法在保护装置剩余寿命预测上具有更高的准确度,可以为保护装置检修运维工作提供理论指导。 展开更多
关键词 继电保护装置 剩余寿命预测 状态评估 改进霜冰优化算法 长短期记忆网络
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An Improved ACO Path Planning Algorithm for Navigation in Weighed Lattice Map 认领 引用
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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
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精密铣削机床效能孪生模型构建及动态优化方法 认领 引用 被引量：1
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作者 梅术龙 谢阳 +2 位作者 张超勇 吴剑钊 刘金锋 《中国机械工程》 EI CAS CSCD 北大核心 2026年第4期875-884,共10页
提出了一种面向机床加工过程的数字孪生动态多目标优化方法。该方法融合历史加工数据与机床实时运行数据,构建由几何模型、物理模型、行为模型和规则模型组成的数字孪生系统,并结合基于Optuna优化的梯度提高回归(Optuna-GBR)预测模型与... 提出了一种面向机床加工过程的数字孪生动态多目标优化方法。该方法融合历史加工数据与机床实时运行数据,构建由几何模型、物理模型、行为模型和规则模型组成的数字孪生系统,并结合基于Optuna优化的梯度提高回归(Optuna-GBR)预测模型与改进的多目标雾凇优化算法(IMORIME)实现加工工艺参数的动态调整。数字孪生系统对切削力波动进行实时监测,当切削力波动超出自适应阈值时,触发动态优化过程,重新生成Pareto解集并通过熵权-逼近理想解排序法(TOPSIS)决策出最优工艺参数组合。实验验证表明,数字孪生系统的动态优化方法使主轴能耗较优化前降低19.99%,切削比能降低29.02%,加工噪声降低11.22%,显著提高加工效率,降低主轴能耗及加工噪声。 展开更多
关键词 数字孪生 动态优化 基于Optuna优化的梯度提高回归 改进多目标雾凇优化算法 自适应阈值
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Optimal Site and Size of Distributed Generation Allocation in Radial Distribution Network Using Multi-objective Optimization 认领 引用 被引量：9
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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)
基于模态分解和LSTM-IDBO-GRU的光伏功率预测研究 认领 引用
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作者 朱婷 颜七笙 《动力工程学报》 CAS CSCD 北大核心 2026年第8期117-127,共11页
为提高光伏发电功率的预测精度,提出了一种基于霜冰优化算法(RIME)、变分模态分解(VMD)、长短期记忆网络(LSTM)和改进蜣螂优化算法(IDBO)优化门控循环单元(GRU)的光伏功率组合预测模型。该方法首先以最小包络熵作为优化算法的适应度函数... 为提高光伏发电功率的预测精度,提出了一种基于霜冰优化算法(RIME)、变分模态分解(VMD)、长短期记忆网络(LSTM)和改进蜣螂优化算法(IDBO)优化门控循环单元(GRU)的光伏功率组合预测模型。该方法首先以最小包络熵作为优化算法的适应度函数,使用RIME对VMD进行优化,寻找本征模态函数(IMF)分量的个数和惩罚因子的最优参数组合。其次,根据过零率将这些分量划分为低频和高频,低频分量使用LSTM模型进行预测,针对高频分量预测精度无法保证的问题,采用多种策略对传统的蜣螂优化算法进行改进,并利用IDBO-GRU模型对高频分量进行预测。最后,将预测结果重构得到光伏发电功率的最终结果。对比实验结果表明:相对于VMD-LSTM-GRU、RIME-VMD-LSTM-GRU和RIME-VMD-LSTM-蜣螂优化算法(DBO)-GRU模型,所提组合模型优于其他模型,各误差评价指标最小,具有更高的预测精度。 展开更多
关键词 光伏发电功率预测 霜冰优化算法 VMD 改进蜣螂优化算法 LSTM GRU
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基于改进霜冰优化算法的混合风-光-波一体化系统阵列优化 认领 引用 被引量：2
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作者 杨博 张芮 +3 位作者 胡袁炜骥 李鸿彪 郜登科 陆海 《南方电网技术》 CSCD 北大核心 2025年第7期50-61,共12页
在国际社会提振气候环境治理的决心与我国“双碳”政策及海洋强国战略的多重驱动下,可再生能源逐步替代化石燃料成为新型发电资源,其中波浪能发电由于资源丰富、绿色环保等优点得到了广泛研究。相较于单一波浪能装置发电系统能源利用率... 在国际社会提振气候环境治理的决心与我国“双碳”政策及海洋强国战略的多重驱动下,可再生能源逐步替代化石燃料成为新型发电资源,其中波浪能发电由于资源丰富、绿色环保等优点得到了广泛研究。相较于单一波浪能装置发电系统能源利用率低的缺点,混合风-光-波一体化系统(hybrid wind-solar-wave system,HWSWS)可最大程度地实现多能互补。因此,提升HWSWS的效率和产量具有深远意义。为充分发挥风、光、波的优势,提出了一种基于改进霜冰优化算法(improved rime optimization algorithm,IRIME)的HWSWS阵列优化布局策略。通过整合Logistic混沌映射、黄金正弦策略和莱维飞行策略对原始的霜冰优化算法(rime optimization algorithm,RIME)进行改进。为了验证IRIME在优化HWSWS阵列方面的有效性,分别在5个HWSWS和9个HWSWS的规模下对阵列进行优化。仿真结果表明,经IRIME优化后的HWSWS的可实现最大功率输出,相较于RIME、粒子群优化算法(particle swarm optimization,PSO)、灰狼优化算法(grey wolf optimizer,GWO)和天鹰优化算法(aquila optimizer,AO),5个HWSWS规模的输出功率分别提高36.2 kW、83.3 kW、27.6 kW和38.0 kW,9个HWSWS规模的输出功率分别提高45.5 kW、191.2 kW、168.5 kW和66.5 kW,证明了IRIME的有效性与优越性。 展开更多
关键词 改进霜冰优化算法 波浪能转换器 漂浮式光伏 漂浮式风电 混合风-光-波一体化系统 SimuNPS软件
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融合参数优化VMD和改进小波阈值的齿轮箱故障信号降噪方法研究 认领 引用 被引量：5
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作者 张海涛 凌海风 +3 位作者 李波 葛靖 康巍 张烨 《机电工程技术》 2025年第17期127-134,157,共8页
针对齿轮箱故障信号在复杂噪声干扰下故障特征提取困难的问题,提出一种融合参数优化变分模态分解(VMD)与改进小波阈值相结合的降噪方法。通过霜冰优化算法(RIME)对VMD的关键参数进行自适应寻优,实现信号频带的精准分离;引入一种基于非... 针对齿轮箱故障信号在复杂噪声干扰下故障特征提取困难的问题,提出一种融合参数优化变分模态分解(VMD)与改进小波阈值相结合的降噪方法。通过霜冰优化算法(RIME)对VMD的关键参数进行自适应寻优,实现信号频带的精准分离;引入一种基于非线性映射的改进小波阈值函数,结合皮尔逊相关系数筛选噪声主导的模态分量进行定向降噪,保留有效故障特征。实验通过HFDZ-330型旋转机械故障实验平台采集的齿轮箱振动信号及高斯仿真信号进行验证。结果表明:相较于传统VMD、经验模态分解(EMD)及小波阈值方法,信噪比和均方误差均显著优化;高频噪声基底在6~12 kHz频段显著降低,齿轮点蚀故障特征频率((1 500±25) Hz)及轴承内圈损伤调制边带((750±82) Hz)清晰显现,证明了算法的有效性和适用性。 展开更多
关键词 齿轮箱 变分模态分解 霜冰优化算法 改进小波阈值 信号降噪
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