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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
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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
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Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
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作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
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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：11
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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：17
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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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An Improved ACO Path Planning Algorithm for Navigation in Weighed Lattice Map 认领 引用 被引量：1
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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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偏远山区配电网的“光储充”双层优化配置策略 认领 引用 被引量：1
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作者 王果 李瑞 +3 位作者 陈鑫 闵永智 郭文凯 苏鹏飞 《高电压技术》 EI CAS CSCD 北大核心 2026年第3期1135-1145,I0013-I0027,共11页
针对偏远山区负荷分散、经济不发达、电压质量差、有功损耗高等问题,为满足新农村建设和新能源汽车下乡需求,通过配置光伏(photovoltaic,PV)、电池储能(battery energy storage system,BESS)和电动汽车充电桩(electric vehicle supply e... 针对偏远山区负荷分散、经济不发达、电压质量差、有功损耗高等问题,为满足新农村建设和新能源汽车下乡需求,通过配置光伏(photovoltaic,PV)、电池储能(battery energy storage system,BESS)和电动汽车充电桩(electric vehicle supply equipment,EVSE),在有效提升配电网供电质量的同时为改善民生提供电力保障。首先,通过概率模型处理偏远山区的PV出力和负荷不确定性,基于蒙特卡洛模拟偏远山区电动汽车(electric vehicle,EV)充电负荷,采用多场景分析法、时段划分法与K-means++聚类法构建综合考虑偏远山区EV充电负荷的源荷时序运行场景;其次,建立偏远山区配电网的“光储充”双层优化配置模型,上层规划层以综合成本最小为目标,确定PV、BESS和EVSE的位置与容量,下层运行层满足电压偏差小、有功损耗低的综合指标,实现PV、BESS和EVSE的最优模拟运行;再次,通过模型转换将双层模型转换为含上下层决策变量的单层多目标模型,提出多目标浣熊优化算法(multi-objective coati optimization algorithm,MOCOA)并对其改进得到改进MOCOA(improved multi-objective coati optimization algorithm,IMOCOA),采用IMOCOA和模糊数学法对转换后的模型求解得到最优配置方案;最后,以基于某偏远山区实际数据改进的IEEE 33节点配电网和西部陕南某偏远山区实际配电网分别进行验证,结果表明,所提配置策略适用于偏远山区配电网“光储充”优化,能在经济掣肘情况下显著提升电压质量、降低有功损耗,所提求解方法的计算速度相比于模型转换前提升70%以上,比NSGA2和MOPSO的求解精度更高。 展开更多
关键词 偏远山区配电网 光储充 双层优化配置 选址定容 模型转换 改进多目标浣熊优化算法
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基于VMD-ICOA-BiGRU模型的油浸式变压器顶层油温预测方法 认领 引用
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作者 王志川 胡益 +3 位作者 邓勇 李林 王涛 罗靖霖 《西华大学学报（自然科学版）》 CAS 2026年第3期108-118,共11页
油浸式变压器是一种被广泛使用的变压器,其顶层油温状态是衡量变压器运行可靠、监测内部绝缘状态、推算变压器内部热点温度的重要参数,对分析变压器潜在故障、开展变压器运维、实现变压器故障早期预警具有重大意义。为提高变压器顶层油... 油浸式变压器是一种被广泛使用的变压器,其顶层油温状态是衡量变压器运行可靠、监测内部绝缘状态、推算变压器内部热点温度的重要参数,对分析变压器潜在故障、开展变压器运维、实现变压器故障早期预警具有重大意义。为提高变压器顶层油温的预测精度,提出一种基于改进浣熊算法(ICOA)优化双向门控循环递归单元(BiGRU)模型的油浸式变压器顶层油温预测方法:首先通过变分模态分解(VMD)将顶层油温原始数据分解为不同频率的模态分量(IMF),以降低非线性数据的预测难度;然后,通过引入Circle混沌映射、反向学习策略、Levy飞行策略对传统的浣熊优化算法进行改进,最大化提高优化算法的全局寻优能力及效率;最后,构建基于BiGRU的变压器顶层油温预测模型,并采用ICOA算法优化其超参数,实现更精细地捕捉时间序列数据的动态变化特征,提高油温预测模型的准确性。利用实测变压器数据,通过算例对比分析,验证了该方法相较于传统预测模型,在油浸式变压器顶层油温预测中能够取得更高的精度。 展开更多
关键词 油浸式变压器 顶层油温 变分模态分解 改进浣熊优化算法 双向门控循环递归单元
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改进浣熊算法的自抗扰控制分岔扰动抑制策略 认领 引用
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作者 孙博睿 周雪松 马幼捷 《可再生能源》 CAS CSCD 北大核心 2026年第1期114-121,共8页
针对Boost级联变换器受参数变化导致输出电压振荡恶化电能质量的问题,文章提出一种改进浣熊算法的自抗扰抑制策略。首先,建立级联变换器的简化离散模型,证明系统中存在倍周期分岔并结合仿真验证,得出引起端口输出电压产生低频振荡是由... 针对Boost级联变换器受参数变化导致输出电压振荡恶化电能质量的问题,文章提出一种改进浣熊算法的自抗扰抑制策略。首先,建立级联变换器的简化离散模型,证明系统中存在倍周期分岔并结合仿真验证,得出引起端口输出电压产生低频振荡是由于系统发生了倍周期分岔扰动;其次,利用线性自抗扰控制器对扰动估计补偿,由于人工整定参数低效且难以保证精确性,引入改进浣熊算法实现控制器参数自适应整定,算法方面,采用自参数化(SPM)混沌映射初始化,增加种群多样性,融合Levy飞行和折射反向学习策略避免算法陷入局部最优,提高参数寻优效率和质量;最后,仿真验证了改进浣熊算法参数配置整定后的控制器能够有效抑制倍周期分岔扰动,改善传输电能质量。 展开更多
关键词 Boost级联变换器 倍周期分岔 改进浣熊优化算法 线性自抗扰控制
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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CLD-COA-ELM的光伏阵列故障诊断方法研究 认领 引用 被引量：11
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作者 张健 赵咪 +1 位作者 黄毅 李景云 《太阳能学报》 EI CAS CSCD 北大核心 2025年第1期632-640,共9页
为提升光伏阵列故障诊断的准确率,提出一种基于改进长鼻浣熊优化算法优化极限学习机的光伏阵列故障诊断方法。首先,分析阵列中光伏组件在发生故障时的输出特性,选择合适的故障特征;其次,针对极限学习机在光伏阵列故障分类时初始权值和... 为提升光伏阵列故障诊断的准确率,提出一种基于改进长鼻浣熊优化算法优化极限学习机的光伏阵列故障诊断方法。首先,分析阵列中光伏组件在发生故障时的输出特性,选择合适的故障特征;其次,针对极限学习机在光伏阵列故障分类时初始权值和阈值的随机性问题,采用长鼻浣熊优化算法求解最优的初始权重和阈值;进一步地,针对长鼻浣熊算法初始参数的随机性和全局搜索能力的局限性问题,通过Circle混沌映射、莱维飞行和动态折射反向学习对该算法进行优化,提高寻优精度和速度;最后,结合光伏阵列故障实验数据,验证故障诊断模型的分类效果。结果表明,对于训练集和测试集数据,该诊断模型提高了故障分类精度,诊断率分别达到100%和98.33%,优于传统极限学习机、BP神经网络、支持向量机和卷积神经网络故障诊断的准确率。 展开更多
关键词 光伏组件 故障分析 特征选择 监督学习 极限学习机 改进长鼻浣熊优化算法
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基于ICOA-XGBoost的光伏阵列复合故障诊断研究 认领 引用 被引量：4
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作者 张子洵 魏业文 +2 位作者 张轲钦 方豪 吴先用 《太阳能学报》 EI CAS CSCD 北大核心 2025年第5期251-259,共9页
为提高光伏阵列复合故障诊断的准确率,提出一种基于改进长鼻浣熊算法(ICOA)优化极端梯度提升(XGBoost)的故障诊断方法。首先,通过分析光伏阵列在不同故障状态下的输出特性,构建一个9维故障特征向量作为模型的输入变量。然后,将结合改进C... 为提高光伏阵列复合故障诊断的准确率,提出一种基于改进长鼻浣熊算法(ICOA)优化极端梯度提升(XGBoost)的故障诊断方法。首先,通过分析光伏阵列在不同故障状态下的输出特性,构建一个9维故障特征向量作为模型的输入变量。然后,将结合改进Circle混沌映射、Levy飞行和t分布随机扰动的ICOA算法与麻雀搜索算法(SSA)、鲸鱼优化算法(WOA)和长鼻浣熊算法(COA)相比较,其在寻优能力、稳定性和收敛速度方面展现出优越性。随后,利用改进的ICOA算法优化XGBoost模型,有效解决了模型初始化参数的设置问题。实验结果显示,结合9维故障特征向量的ICOA-XGBoost模型在故障诊断精度上达到97.23%,优于其他对比模型,证实了所提方法的可行性和有效性。 展开更多
关键词 光伏阵列 故障诊断 改进长鼻浣熊算法 极端梯度提升
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基于ICOA算法的泵控液压马达PID调速系统 认领 引用 被引量：3
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作者 杨焕峥 崔业梅 +1 位作者 薛洪惠 徐玲 《机床与液压》 北大核心 2025年第5期101-106,共6页
为了提高泵控液压马达PID调速系统的速度和精度,通过建立数学模型和Simulink仿真系统,确定了变量泵控定量液压马达系统以液压泵摆角为输入的调速控制回路的传递函数;针对传统PID调速系统在速度和精度方面的局限性,引入一种改进的长鼻浣... 为了提高泵控液压马达PID调速系统的速度和精度,通过建立数学模型和Simulink仿真系统,确定了变量泵控定量液压马达系统以液压泵摆角为输入的调速控制回路的传递函数;针对传统PID调速系统在速度和精度方面的局限性,引入一种改进的长鼻浣熊优化算法(ICOA),该算法结合了反向学习差分进化和萤火虫扰动策略以提高系统性能。在CEC2022函数的性能测试中,相比长鼻浣熊优化算法等5种算法,ICOA算法表现优异,它在单峰、多峰、复合且多模态的函数上均表现出较好的收敛速度、寻优精度和鲁棒性。最后,通过仿真验证,ICOA算法在泵控液压马达PID调速性能优化方面具有更好的效果,能够更有效地使系统响应达到期望的状态。 展开更多
关键词 泵控液压马达 PID调速系统 改进浣熊优化算法 控制性能
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基于DGA的LightGBM-ICOA-CNN变压器故障诊断方法 认领 引用 被引量：1
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作者 孙涛 陈鑫 +3 位作者 杜民生 郭文凯 张佳 李亚兴 《电气工程学报》 CSCD 北大核心 2025年第6期459-468,共10页
为提高基于深度学习的变压器故障诊断精度,提出了基于油中溶解气体分析(Dissolved gas analysis,DGA)的LightGBM-ICOA-CNN变压器故障诊断方法。首先,基于变压器油中溶解气体含量对变压器特征变量进行丰富,利用轻量梯度提升机算法(Light ... 为提高基于深度学习的变压器故障诊断精度,提出了基于油中溶解气体分析(Dissolved gas analysis,DGA)的LightGBM-ICOA-CNN变压器故障诊断方法。首先,基于变压器油中溶解气体含量对变压器特征变量进行丰富,利用轻量梯度提升机算法(Light gradient boosting machine,LightGBM)量化其重要性,实现特征变量优选;其次,引入改进浣熊优化算法(Improved coati optimization algorithm,ICOA)对卷积神经网络(Convolutional neural network,CNN)的学习率、卷积核大小与数量、全连接层神经元数量等超参数实现优化,提高模型诊断结果的准确率;最后,通过算例分析对建立的LightGBM-ICOA-CNN方法性能进行评估,验证了所提方法对变压器故障诊断的有效性,且收敛性较好,精度较高。 展开更多
关键词 变压器 故障诊断 轻量梯度提升机 特征变量 改进浣熊优化算法 卷积神经网络
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改进长鼻浣熊优化最小二乘支持向量机的MMC子模块故障诊断方法 认领 引用
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作者 张彼德 汪瑞杰 +2 位作者 曾杰 何恒志 王泽林 《电力系统及其自动化学报》 CSCD 北大核心 2025年第12期96-105,共10页
为实现模块化多电平换流器子模块开路故障诊断,提出一种改进长鼻浣熊优化算法结合最小二乘支持向量机的故障诊断方法。该方法针对长鼻浣熊优化算法的初始化、探索和开发3个阶段分别引入折射反向学习策略、levy飞行策略、螺旋搜索机制和... 为实现模块化多电平换流器子模块开路故障诊断,提出一种改进长鼻浣熊优化算法结合最小二乘支持向量机的故障诊断方法。该方法针对长鼻浣熊优化算法的初始化、探索和开发3个阶段分别引入折射反向学习策略、levy飞行策略、螺旋搜索机制和E分布随机扰动,以提升算法的收敛速度和全局搜索能力,并找到最小二乘支持向量机中的惩罚系数Z和核函数参数g的最优组合。首先在Matlab/Simulink中搭建模块化多电平换流器子模块模型,以子模块开路故障条件下的桥臂电流作为输入量,对改进长鼻优化算法优化的最小二乘支持向量机模型与其他优化算法优化的最小二乘支持向量机模型进行对比分析;其次,研究Z和g对模块化多电平换流器子模块故障诊断准确率的影响。结果表明,本文提出的改进长鼻浣熊优化算法优化最小二乘支持向量机的方法在模块化多电平换流器子模块故障诊断准确率最高,且借助智能优化算法进行参数寻优非常高效。 展开更多
关键词 模块化多电平换流器 开路故障 改进长鼻浣熊优化算法 惩罚系数 核函数参数 最小二乘支持向量机 故障诊断
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融合ICOA及PSM的轮毂电机多场耦合噪声优化 认领 引用
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作者 吴华伟 李蒗 +2 位作者 李智 曾运运 彭建平 《重庆交通大学学报（自然科学版）》 CAS CSCD 北大核心 2025年第7期23-32,共10页
为削弱轮毂电机电磁振动噪声,以18槽16极14吋永磁轮毂电机为例,提出了一种融合改进浣熊优化算法(ICOA)及参数扫描法(PSM)的结构优化设计方法。建立基于PSM的齿槽转矩数据库,解析定子辅助槽数量对齿槽转矩的影响机理;构建基于自适应边界... 为削弱轮毂电机电磁振动噪声,以18槽16极14吋永磁轮毂电机为例,提出了一种融合改进浣熊优化算法(ICOA)及参数扫描法(PSM)的结构优化设计方法。建立基于PSM的齿槽转矩数据库,解析定子辅助槽数量对齿槽转矩的影响机理;构建基于自适应边界和淘汰机制的改进浣熊优化算法,设计基于ICOA的求解器对轮毂电机辅助槽进行优化,并与基于COA、MA、SSA的3种求解器对比寻优性能;搭建轮毂电机的结构场、电磁场及声场等多物理场耦合仿真模型,对比定子电枢结构优化前后的噪声声压级。研究结果表明:ICOA求解器在收敛速度和结果精度上优于其他求解器;优化后齿槽转矩幅值削弱59.08%;在空载时,电机转轴轴向的振动削弱了9.916×103mm/s2,转轴径向的振动削弱了2.1919×104mm/s2,A计权声压级减小了3.818 dB;在负载时,转轴轴向的振动削弱了4.8459×104mm/s2,转轴径向的振动削弱了4.4226×104mm/s2,A计权声压级减小了7.648 dB;7倍频振动得到有效抑制,噪声总体水平从70 dB级削弱到60 dB级,提高了驾乘人员的安全性和舒适性。 展开更多
关键词 车辆工程 轮毂电机 噪声优化 改进浣熊优化算法 参数扫描法 多场耦合
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调焦机上下料六轴机械臂轨迹优化 认领 引用
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作者 杨洪涛 李天乐 +2 位作者 穆莉莉 韩明 王海超 《机械工程与自动化》 2025年第2期24-27,共4页
以自动调焦机中的上下料机械臂系统为研究对象,针对机械臂工作过程中需满足轨迹光滑、生产节拍和降低能耗的要求,对其轨迹优化展开研究。首先,分析了自动调焦机上下料系统组成与技术要求;其次,构造了以4-5-4分段多项式为曲线模型的机械... 以自动调焦机中的上下料机械臂系统为研究对象,针对机械臂工作过程中需满足轨迹光滑、生产节拍和降低能耗的要求,对其轨迹优化展开研究。首先,分析了自动调焦机上下料系统组成与技术要求;其次,构造了以4-5-4分段多项式为曲线模型的机械臂关节空间轨迹;最后,提出采用改进长鼻浣熊算法对机械臂进行能耗最优轨迹优化。仿真结果表明:由算法优化后得到的机械臂轨迹连续平滑且满足约束,能量消耗明显降低,满足自动调焦机上下料轨迹要求。 展开更多
关键词 自动调焦机 上下料六轴机械臂 改进长鼻浣熊算法 能耗轨迹优化
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