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Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
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作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
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Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 Huanan Yu Hangyu Li +1 位作者 He Wang Shiqiang Li 《Energy Engineering》 EI 2024年第6期1535-1555,共21页
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim... The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach. 展开更多
关键词 Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network
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Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 姜文英 林焰 +1 位作者 陈明 于雁云 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期565-570,共6页
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car... Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach. 展开更多
关键词 cargo oil tank optimization design nonlinear programming improved particle swarm optimization（PSO）algorithm fuzzy constraint construction feasibility degree
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Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm 认领 引用
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作者 Yi-Han Wang Hai-Feng Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期352-363,共12页
Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded p... Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm. 展开更多
关键词 magnetized plasma photonic crystals improved particle swarm optimization algorithm nonreciprocal ultra-wide band absorption angular insensitivity
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Improved Particle Swarm Optimization Algorithm to Solve the Problem of Layout Optimization of Electric Vehicle Charging Stations 认领 引用
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作者 QI Lin YAO Jian WANG Xin-yue 《Journal of Highway and Transportation Research and Development(English Edition)》 2018年第2期96-103,共8页
In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorith... In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorithm,this study puts forward a method to improve the global search ability of the adaptive parameter adjustment strategy.A simulation is performed accordingly.Results show that when solving the layout optimization problem for charging stations,the improved adaptive hybrid algorithm that combines the k-center and cloud model particle swarm algorithm outperforms the original cloud model particle swarm algorithm and the basic particle swarm optimization algorithm.The improved algorithm is thus effective. 展开更多
关键词 Traffic engineering layout optimization of electric vehicle charging station improved particle swarm optimization algorithm particle swarm optimization algorithm cloud model Voronoi diagram
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Robot stereo vision calibration method with genetic algorithm and particle swarm optimization 认领 引用 被引量：2
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作者 汪首坤 李德龙 +1 位作者 郭俊杰 王军政 《Journal of Beijing Institute of Technology》 EI CAS 2013年第2期213-221,共9页
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ... Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation. 展开更多
关键词 robot stereo vision camera calibration genetic algorithm （GA） particle swarm opti-mization （PSO） hybrid intelligent optimization
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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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Error modeling and flexure-based calibration of large-aperture optical adjustment mechanisms using an improved particle swarm optimization algorithm 认领 引用
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作者 Kaiqi Zhang Quantang Fan +4 位作者 Zhigang Liu Pengqian Yang Ze Zhang Siyu Xu Jianqiang Zhu 《High Power Laser Science and Engineering》 SCIE EI CAS CSCD 2025年第6期256-270,共15页
Meter-scale large-aperture gratings are essential in petawatt-class picosecond laser systems.Their grating mounts must support heavy-load arrays and high alignment accuracy due to high energy density and long beam pat... Meter-scale large-aperture gratings are essential in petawatt-class picosecond laser systems.Their grating mounts must support heavy-load arrays and high alignment accuracy due to high energy density and long beam paths.However,nonlinear errors from parasitic motions and transmission gaps can significantly degrade precision.This study presents a kinetostatic modeling and error calibration framework for the grating mount,incorporating an improved particle swarm optimization(PSO) algorithm.The nonlinear error model combines energy-based and pseudo-rigid-body methods,with equivalent representations of structural gaps and parasitic motions.To capture multi-source nonlinear interactions,a global-dynamic multi-subgroup PSO enhances calibration via coordinated global exploration and local refinement.Experiments indicate that,compared with conventional models,first-round compensation reduces average errors by over65.4%,79.8% and 74.8% in rotation,tip and tilt,respectively.The method integrates nonlinear pose modeling,unified gap representation and an enhanced PSO strategy,offering an effective solution for error compensation in meter-scale,heavy-load compliant mechanisms. 展开更多
关键词 identification algorithm improved particle swarm optimization inertial confinement fusion kinematic calibration parallel compliant mechanisms
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Improved algorithms to plan missions for agile earth observation satellites 认领 引用 被引量：3
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作者 Huicheng Hao Wei Jiang Yijun Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期811-821,共11页
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell... This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective. 展开更多
关键词 mission planning immune clone algorithm hybrid genetic algorithm （EA） improved ant colony algorithm general particle swarm optimization （PSO） agile earth observation satellite （AEOS）.
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Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm 认领 引用 被引量：24
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作者 Leijiao Ge Yuanliang Li +2 位作者 Jun Yan Yuqian Wang Na Zhang 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第6期1490-1499,共10页
To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)mo... To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN. 展开更多
关键词 Integrated energy system(IES) load prediction chaos optimization algorithm(COA) improved particle swarm optimization(IPSO) Pearson correlation coefficient wavelet neural network(WNN)
基于改进免疫粒子群算法的混合储能容量优化 认领 引用 被引量：3
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作者 李练兵 王兰超 +2 位作者 景睿雄 肖亚泽 杨少波 《电源学报》 CSCD 北大核心 2026年第2期208-215,共8页
为了提高微电网运行的经济性和稳定性,需要根据气象信息和负荷信息对微电网的容量进行合理优化。为此,建立分布式电源的数学模型,根据系统的约束条件和运行策略,以分布式电源的数量作为优化变量,以总成本最低为目标函数,利用改进的免疫... 为了提高微电网运行的经济性和稳定性,需要根据气象信息和负荷信息对微电网的容量进行合理优化。为此,建立分布式电源的数学模型,根据系统的约束条件和运行策略,以分布式电源的数量作为优化变量,以总成本最低为目标函数,利用改进的免疫粒子群优化算法对微电网的容量进行优化。首先,利用正态分布进行初始化,增加种群多样性。然后,利用非线性惯性因子、自适应惯性权重和混沌扰动算子提高算法的收敛速度和收敛精度。实验结果表明,所提方法具有合理性,可以有效降低投资成本,为微电网的容量优化提供参考价值。 展开更多
关键词 微电网 容量优化 改进免疫粒子群优化算法 经济性
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面向多无人机物流配送的双层任务规划方法 认领 引用 被引量：11
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作者 王飞 杨清平 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2026年第1期94-103,共10页
多无人机任务协同规划与配送路径规划是城市无人机物流配送的核心内容,两者相互耦合,需要进行一体化研究。为保障安全、高效完成多无人机物流配送任务,采用栅格法对三维城市超低空间进行环境建模,阐述了栅格危险度计算方法。构建一种无... 多无人机任务协同规划与配送路径规划是城市无人机物流配送的核心内容,两者相互耦合,需要进行一体化研究。为保障安全、高效完成多无人机物流配送任务,采用栅格法对三维城市超低空间进行环境建模,阐述了栅格危险度计算方法。构建一种无人机配送线路及航迹协同规划的双层规划模型,在上层规划模型中,考虑无人机载重及最大航程约束,以延迟惩罚代价最小为目标,引入遗传算法来确定无人机配送顺序;在下层规划模型中,考虑无人机性能约束,以时效性代价最小、无人机高度变化及栅格危险度最小为目标,提出一种综合改进粒子群优化(CIPSO)算法,求解无人机飞行路径。进行算例仿真分析,结果表明:与粒子群优化(PSO)算法、改进加速因子粒子群优化(ICPSO)算法相比,CIPSO算法总代价分别下降了65.00%和38.41%,所建模型与所提算法是可行的和有效的。 展开更多
关键词 物流无人机 任务分配 路径规划 双层规划模型 改进粒子群优化算法
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基于改进PSO-BO-BP的拖拉机双燃料发动机性能预测 认领 引用 被引量：1
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作者 陈晖 王冰心 +1 位作者 黄镇财 计端 《农机化研究》 北大核心 2026年第1期268-276,共9页
为提高拖拉机双燃料发动机性能与排放预测模型的性能,提出了一种融合改进粒子群优化算法(IMPSO)、贝叶斯优化(BO)和反向传播(BP)的协同预测模型(IMPSO-BO-BP)。基于发动机台架试验数据,通过整合IMPSO全局搜索、BO概率推理和BP梯度更新机... 为提高拖拉机双燃料发动机性能与排放预测模型的性能,提出了一种融合改进粒子群优化算法(IMPSO)、贝叶斯优化(BO)和反向传播(BP)的协同预测模型(IMPSO-BO-BP)。基于发动机台架试验数据,通过整合IMPSO全局搜索、BO概率推理和BP梯度更新机制,构建多尺度优化模型。结果表明:BO解析了神经网络隐含层维度与学习率的非线性耦合效应,确定隐含层神经元数量24、学习率0.00215为最优参数组合,表明模型复杂度与学习率调控对泛化性能的协同约束作用;性能预测中,IMPSO-BO-BP对制动热效率(BTE)和制动燃料消耗率(BSFC)的预测平均绝对百分比误差(MAPE)与均方根误差(RMSE)较BO-BP模型降低25%~40%,R2提升至0.995及以上,验证了其对物理主导型非线性关系的高精度建模能力;排放预测方面,模型对CO、NOx和HC的MAPE为3.403%、5.223%、3.413%,R2达0.9925、0.9942、0.9946,RMSE为56.429、45.709、335.322,虽精度略低于性能参数预测,但较BO-BP模型仍提升显著。研究证实多算法协同机制通过全局优化与局部收敛的互补效应,可显著提升模型精度和鲁棒性,为拖拉机双燃料发动机多目标优化控制和低排放设计提供了可靠的建模工具。 展开更多
关键词 双燃料发动机 性能预测 BP神经网络 改进粒子群优化算法
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基于径流预测的流域小水电群可调能力优化 认领 引用 被引量：1
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作者 何桂雄 张新鹤 +1 位作者 谢学渊 徐勇 《水电能源科学》 北大核心 2026年第1期217-221,共5页
小水电具有小容量、多点分布、流域相关性强等特点,传统“随流发电”模式具有无序性,小水电群灵活可调价值未得到充分释放,可调能力评估与优化是流域小水电群支撑电网调峰、风光电消纳的关键。在流域小水电群径流预测基础上,提出以小水... 小水电具有小容量、多点分布、流域相关性强等特点,传统“随流发电”模式具有无序性,小水电群灵活可调价值未得到充分释放,可调能力评估与优化是流域小水电群支撑电网调峰、风光电消纳的关键。在流域小水电群径流预测基础上,提出以小水电当前水位最大发电流量下泄对应出力为可调出力上限,以生态装机容量对应出力为下限,确定了小水电可调节容量区间,进而构建了流域小水电群可调容量优化模型并提出改进粒子群求解算法。以金溪流域的良浅、大言、孔头、范厝、高唐5座串联径流式电站为例进行降雨-径流过程模拟,分析小水电群库容与其发电、入库流量耦合关系,计算流域小水电群最优出力及可调节出力区间并进行优化求解。结果表明,优化后可调容量区间增大,调节能力上限提高了12.1%,实际出力比优化前提高了14.6%。优化后出力方式可支撑电网在更大区间调整小水电出力,为电网调度部门挖掘小水电资源灵活性价值,发挥其调峰和消纳能力提供了决策支撑。 展开更多
关键词 径流预测 小水电群 可调能力 改进粒子群算法 优化调度
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基于IPSO-BP-FuzzyPID的爬模液压缸同步控制研究 认领 引用 被引量：1
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作者 卢宁 胡信凯 张辉 《机床与液压》 北大核心 2026年第5期119-126,共8页
针对爬模爬升过程中液压缸同步精度低和控制效果欠佳的问题,提出IPSO-BP-FuzzyPID与均值耦合同步控制策略。分析爬模结构及液压原理,设计均值耦合控制策略,提出引入同步误差动态调整权重和学习因子的粒子群算法,修正模糊神经网络在寻优... 针对爬模爬升过程中液压缸同步精度低和控制效果欠佳的问题,提出IPSO-BP-FuzzyPID与均值耦合同步控制策略。分析爬模结构及液压原理,设计均值耦合控制策略,提出引入同步误差动态调整权重和学习因子的粒子群算法,修正模糊神经网络在寻优过程中收敛缓慢、易陷入局部最小值的不足,并利用模糊神经网络的自学习能力,对PID控制器的参数进行整定。最后,采用IPSO-BP-FuzzyPID与均值耦合同步控制相结合的控制器,对爬模液压缸组进行AMESim/Simulink联合仿真,并将其与FuzzyPID和BP-FuzzyPID控制策略进行对比。结果表明:相比FuzzyPID与BP-FuzzyPID控制策略,IPSO-BP-FuzzyPID振荡幅度更小,振荡时间缩短73%和56%;FuzzyPID与BP-FuzzyPID达到预期位移分别需要39.5、38.2 s,而IPSO-BP-FuzzyPID仅需23.8 s,缩短了40%和37%;IPSO-BP-FuzzyPID将最大跟踪误差从0.0465、0.0308 m降低至0.0151 m,分别降低了0.0314、0.0157 m。IPSO-BP-FuzzyPID控制器减小了振荡幅度,提高了同步精度,提高了爬升效率。该策略为爬模等工程机械的高精度同步控制提供了有效的解决方案。 展开更多
关键词 爬模液压缸 同步控制 改进粒子群算法 均值耦合策略 联合仿真
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城市环境下多无人机双层路径规划算法 认领 引用
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作者 夏雨奇 黄炎焱 +1 位作者 董李 翟文杰 《西北工业大学学报》 EI CAS CSCD 北大核心 2026年第1期92-101,共10页
针对复杂城市作战环境下多无人机对敌多目标快速打击任务中需同时考虑能耗、飞行风险及安全距离等多种优化指标,现有算法在路径规划中存在搜索速度慢、易陷入局部最优等问题,提出一种基于A* 算法与改进粒子群算法(PSO)的双层优化方... 针对复杂城市作战环境下多无人机对敌多目标快速打击任务中需同时考虑能耗、飞行风险及安全距离等多种优化指标,现有算法在路径规划中存在搜索速度慢、易陷入局部最优等问题,提出一种基于A* 算法与改进粒子群算法(PSO)的双层优化方法。该方法利用A* 算法生成初始路径,作为粒子群算法的初始解,提升搜索初期的效率;同时引入带变异结构的粒子机制,增强粒子群在解空间中的全局搜索能力,有效缓解早熟收敛问题。仿真实验结果表明,该双层优化算法在提升搜索效率的同时,显著改善了陷入局部最优的问题。研究结果验证了所提算法在复杂城市环境下多无人机路径规划任务中的可行性和有效性。 展开更多
关键词 多无人机 路径规划 改进粒子群算法 A*算法
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采用改进多目标粒子群算法的 海岛综合能源系统优化调度 认领 引用
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作者 戴红伟 胡青怡 +2 位作者 孙靖 王金龙 杨玉 《西安交通大学学报》 EI CAS CSCD 北大核心 2026年第6期188-200,共13页
为解决海岛多样化用能需求的有效供给问题,弥补传统调度模型难以兼顾多负荷需求与可再生能源消纳的不足,提出一种包含电、热、冷、水、氢5种负荷的海岛综合能源系统优化调度方法。以最小化综合经济成本、最大化可再生能源出力为优化目标... 为解决海岛多样化用能需求的有效供给问题,弥补传统调度模型难以兼顾多负荷需求与可再生能源消纳的不足,提出一种包含电、热、冷、水、氢5种负荷的海岛综合能源系统优化调度方法。以最小化综合经济成本、最大化可再生能源出力为优化目标,考虑能量平衡、储能限制、转换效率等约束,构建海岛综合能源系统优化调度模型。针对传统多目标粒子群算法的缺陷,提出一种改进多目标粒子群算法(IMOPSO),引入自适应惯性权重更新机制与飞行参数动态调整策略,以增强算法的全局搜索能力与局部开发能力,同时结合领导者择优选择机制与变异算子,提高非支配解集的多样性和分布均匀性,从而优化算法性能。标准测试函数对比实验表明:所提出的IMOPSO算法的世代距离、逆世代距离指标均表现最好,超体积指标在多个函数上取得最高值,证明其在多样性、分布性和收敛性的显著优势。夏季和冬季两个典型日场景的验证表明,与传统多目标粒子群算法和其他对比算法相比,所提算法在综合经济成本指标上分别降低了1.80%、4.37%以上,在能源出力指标上分别提高了18.52%、1.60%以上,能满足海岛多类型负荷需求。 展开更多
关键词 海岛综合能源 优化调度 多目标优化 改进粒子群算法 可再生能源
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基于改进粒子群算法的三元锂离子电池荷电状态估计 认领 引用
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作者 朱茂桃 肖晓锋 +1 位作者 刘欢 吴佘胤 《江苏大学学报（自然科学版）》 CAS 北大核心 2026年第1期79-87,共9页
针对卡尔曼滤波算法估计锂离子电池荷电状态存在精度较低的问题,提出了一种基于改进粒子群算法(IPSO)优化双卡尔曼滤波算法(DKF)的方法.在粒子群算法的基础上,引入一种蜘蛛移动策略的黑寡妇优化算法(BWOA)对粒子速度更新方式优化.采用... 针对卡尔曼滤波算法估计锂离子电池荷电状态存在精度较低的问题,提出了一种基于改进粒子群算法(IPSO)优化双卡尔曼滤波算法(DKF)的方法.在粒子群算法的基础上,引入一种蜘蛛移动策略的黑寡妇优化算法(BWOA)对粒子速度更新方式优化.采用改进粒子群算法优化双卡尔曼滤波算法的噪声协方差矩阵.依据试验数据,基于二阶电阻-电容电路(RC)模型完成参数辨识和电池荷电状态(SOC)估计.对比标准卡尔曼滤波算法与经粒子群算法优化的卡尔曼滤波算法在参数辨识和荷电状态估计方面的结果.结果表明:改进后的算法在参数辨识和荷电状态估计精度方面显著提升,且具有更强的抗干扰能力,其中参数辨识估计精度提高范围为7.9%~38.5%,荷电状态估计精度提高范围为41.0%~51.4%. 展开更多
关键词 锂离子电池 改进粒子群算法 参数辨识 电池荷电状态估计 双卡尔曼滤波
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基于多目标约束的狭小空间便携式机器人轨迹优化方法 认领 引用
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作者 刘金锋 顾世民 +4 位作者 张占虎 李苏 陈宇 王学敏 钱天龙 《中国机械工程》 EI CAS CSCD 北大核心 2026年第6期1508-1517,1530,共10页
针对船舶狭小空间焊接时作业空间受限、焊枪姿态约束多、轨迹干涉风险高、焊接可达性差等挑战,以机械臂最短时间完成焊接为目标,提出基于改进遗传粒子群算法(IGA-PSO)的时间优化方案以优化焊接轨迹。构建便携式机器人、工件和场景的三... 针对船舶狭小空间焊接时作业空间受限、焊枪姿态约束多、轨迹干涉风险高、焊接可达性差等挑战,以机械臂最短时间完成焊接为目标,提出基于改进遗传粒子群算法(IGA-PSO)的时间优化方案以优化焊接轨迹。构建便携式机器人、工件和场景的三维模型,明确机器人运动逻辑,建立焊接轨迹模型并确定焊接工艺;综合考虑焊接时间和可达性,设计多目标约束适应度函数,并建立时间优化与可达率目标函数;结合遗传和粒子群算法,对惯性权重引入线性递减和指数递减机制,对学习因子设计探索、开发和收敛阶段,对变异进行非线性调整,以提高算法的性能。通过算法测试、仿真验证和现场验证对该方法进行了案例验证,结果表明,优化后的机械臂位移、速度、加速度曲线平滑、无突变,且焊接可达率达到90%,验证了IGA-PSO算法的有效性。 展开更多
关键词 多目标优化 狭小空间焊接 路径优化 改进遗传粒子群算法 自适应机制
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港口无人集卡与自动引导车联合调度双层优化模型及算法设计 认领 引用
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作者 初良勇 张一鸣 +2 位作者 高子健 陈秀乾 杜嘉音 《哈尔滨工程大学学报》 EI CAS CSCD 北大核心 2026年第3期694-707,共14页
为提升自动化集装箱码头运输调度的灵活性与资源适配能力,解决自动引导车磁钉导引封闭运输难以适配码头-物流园区长距离作业的问题,本文将无人集卡调度环节考虑进自动化集装箱码头综合运输调度流程中,提出自动化集装箱码头无人集卡、自... 为提升自动化集装箱码头运输调度的灵活性与资源适配能力,解决自动引导车磁钉导引封闭运输难以适配码头-物流园区长距离作业的问题,本文将无人集卡调度环节考虑进自动化集装箱码头综合运输调度流程中,提出自动化集装箱码头无人集卡、自动引导车联合调度任务派遣与路径优化双层模型。根据模型特征,设计改进遗传算法求解任务派遣模型,设计优化粒子群算法求解无冲突路径结果。算例实验表明:自动引导车与无人集卡在不同配置比下联合调度总成本不同,在配置比相近且自动引导车利用率较大情况下获得最优目标结果;算法对比实验表明,改进遗传算法以及优化粒子群算法能够有效求解该模型,其运算性能优于常规算法,对于未来自动化码头可持续发展以及相关企业、港口集团进行资源良性整合具有重要参考价值。 展开更多
关键词 自动化集装箱码头 无人集装箱卡车 自动引导车 联合调度 双层优化模型 分阶段 改进遗传算法 优化粒子群算法
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