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Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

ASIAN JOURNAL OF CONTROL 2023年第2期25卷 1542-1554页

作者： Zhang, Bo Jiang, Aipeng Qi, Yanying Jiang, Jiaji Wang, Haokun Kong, Jundong Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China

In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.

关键词： moving horizon estimation normalization optimization algorithm quadratic programming window length

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optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第15期30卷 44536-44552页

作者： Benghanem, Mohamed Lekouaghet, Badis Haddad, Sofiane Soukkou, Ammar Islamic Univ Madinah Fac Sci Phys Dept Madinah Saudi Arabia MSB Jijel Univ Elect Dept RE Lab Jijel Algeria

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value 7.73006208 x 10(-4), and in the double diode model, an RMSE of value 7.445111655 x 10(-4) has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.

关键词： optimization algorithm Photovoltaic modules Rao-1 algorithm Chaotic PV parameters

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Shifting strategy optimization of the thermal work roll in hot strip rolling based on three particle swarms and differential evolution algorithm

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IRONMAKING & STEELMAKING 2023年第5期50卷 517-528页

作者： He, Hainan Ding, Jijie Xu, Dong Wang, Xiaochen Yang, Quan Zhai, Dejia Shao, Jian Univ Sci & Technol Beijing Natl Engn Technol Res Ctr Flat Rolling Equipment Beijing 100083 Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling & Intelligent Mfg Beijing Peoples R China

During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the free shifting of the work rolls. In this paper, the thermal crown evaluation index and the thermal expansion simulation model of the work roll are established, and the influence of different roll shifting parameters on the thermal crown of the roll in the service cycle of the work roll is analyzed. A special roll shifting strategy of the downstream stand is designed, and intelligent optimization is carried out with the goal of thermal roll shape and uniform wear of work rolls. The optimized roll shifting strategy can significantly improve the strip shape quality.

关键词： Hot continuous rolling thermal expansion shifting strategy optimization algorithm fast-paced rolling strip profile work roll free-shifting roll

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Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第9期30卷 22396-22412页

作者： Zhao, Long Zhao, Xinbo Li, Yuanze Shi, Yi Zhou, Hanmi Li, Xiuzhen Wang, Xiaodong Xing, Xuguang Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471000 Henan Peoples R China Henan Univ Sci & Technol Coll Agr Luoyang 471000 Henan Peoples R China Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid Area Yangling 712100 Shaanxi Peoples R China

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources;this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO;the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R-2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

关键词： Reference crop evapotranspiration KELM algorithm optimization algorithm Factor importance Modeling Arid and semiarid regions

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Robotic arm trajectory optimization based on multiverse algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2776-2792页

作者： Liu, Junjie Wang, Hui Li, Xue Chen, Kai Li, Chaoyu Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing 210016 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Beijing Peoples R China

For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and conver-gence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.

关键词： trajectory planning optimization algorithm optimal time optimal energy consumption optimal impact

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Performance analysis of an optimization management algorithm on a multi-generation small size power plant 71

Performance analysis of an optimization management algorithm...

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71st Conference of the Italian-Thermal-Machines-Engineering-Association (ATI)

作者： Danti, Piero Pezzola, Lorenzo Magnani, Sandro Yanmar R&D Europe I-50125 Florence Italy

In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also emphasizing the environmental impact. In order to measure this impact, the key parameter is the CO2 emission in the atmosphere. The most powerful mean to satisfy this compromise between economic benefits and emission decrease is represented by the concept of Smart Grid. A Smart Grid implies a joint participation between information network and electric grid. In order to acquire the data from the electric grid, transmit them through the IT network, compute and translate them into commands to the plant devices, an 'intelligent brain' is necessary. In order to embed a small local network in the larger VPP a delocalized intelligent device is necessary, able to interface with the Smart Grid. An optimization algorithm performs this function of intelligent delocalized brain by setting different set-points for the energy devices on field. In this paper a purposefully developed optimization algorithm is described, with the aim of optimizing the operations of an existent trigeneration plant managing both RES and fossil energy sources. The plant analysed is a real plant located in central Italy, provided by several generators (PV, CHP, absorption chiller, electric chiller, gas boiler and a wind turbine). The results are yielded by a MATLAB/Simulink simulation tool, where all plant devices are characterized by datasheet information and on-field measurements. The benefits evaluation of the algorithm optimized management is obtained by embedding inside Simulink the optimization logic and executing it during the simulation runtime. The performance is compared with conventional thermal led management operations simulated in the same platform. The comparison is mainly based on economic costs but also considers CO2 emissions and primary energy consumption. The analysis tak

关键词： CHP optimization algorithm Genetic algorithm Thermal Led

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Optimizing energy constrained target localization and tracking with radial bias and seeker optimization algorithms in wireless sensor networks

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METHODSX 2025年 14卷

作者： Yazhinian, S. Famila, S. Jose, P. Mahendar, A. Sofia, R. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai India CMR Tech Campus Hyderabad India Manakula Vinayagar Inst Technol Pondicherry India

The standard localization approach is characterized by a fixed position distribution of the anchor nodes, which cannot be dynamically modified based on the deployment environment. This paper proposes a novel approach combining Radial Bias (RB) with the Seeker optimization algorithm (SOA) to address the challenges of energy-constrained target localization and tracking. The RB technique enhances localization accuracy by refining the position estimates of the target, while the SOA optimizes sensor deployment and data transmission paths to minimize energy consumption. By integrating these two methodologies, ensures a balance between precision in tracking and energy efficiency. Extensive simulations shown this technique surpasses existing methods in terms of both accuracy in determining the location and the duration of network operation. This makes it attractive option for applications of energy-constrained WSNs. The investigation examines the outcome of the particle count in the RBSO algorithm, specifically for values of 5, 10, 15, 20, and 25. The simulation results show that the recommended strategy decreases particles, speeds up positioning and tracking, and maintains target localization and tracking accuracy. It is seen that the proposed RadB_SOA achieves 12.4 % of transmission error, 14.6 % of ranging error, 96.3 % of localization coverage, 98.65 % of PDR, and 21.56 % of energy consumption. center dot The Radial Bias-Seeker optimization algorithm (RadB_SOA) suggested enhances the precision in target localization and optimizes energy usage in wireless sensor networks. center dot Simulation outcomes reveal improved tracking accuracy, minimized transmission and ranging errors, as well as increased localization coverage over current techniques. center dot The research presents an extensive evaluation of particle count fluctuations in RBSO, demonstrating enhanced positioning speed and precision with network efficiency.

关键词： Coverage Energy efficiency optimization algorithm Target localization Tracking Received signal strength

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The Reliability optimization and algorithm in Network with Expected- Path Length Constraint 2

The Reliability Optimization and Algorithm in Network with E...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Lin, Wei Zhang, Zuyuan Shao, Fangming East China Univ Sci & Technol Dept Math Shanghai Peoples R China

ISBN: (纸本)9781467390262

Reliability is a parameter of evaluating network performance and expected path length can index the contribution of s-t paths to network reliability. It is meaningful to observe the important part of network performance in light of the reliability and path length. In this paper, we attempt to reveal the important part of network performance based on reliability. Conversely we consider the optimization problem of two-terminal reliability with expected-path length constraint. Next, we transform the problem into searching delta-maximum graph, in which its expected path length is not greater than delta(0) and reliability is maximum. Further, we find a rule of removing redundant subgraphs and propose an algorithm to search the optimal solution. Simulation shows the effectiveness of the proposed algorithm.

关键词： network reliability optimization algorithm expected-path length

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Multi-Agent Chaos Particle Swarm optimization algorithm of Thrust Allocation for dynamic positioning vessels 34

Multi-Agent Chaos Particle Swarm Optimization Algorithm of T...

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第三十四届中国控制会议

作者： XU Guang-chi ZHAO Da-wei ZHANG Kai-wei GE Jing-hua College of Mechanics Engineering Heilongjiang Agricultural Engineering Vocational College College of Automation Harbin Engineering University

ISBN: (纸本)9781467374439

As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated as nonlinear optimization problems. A chaos Particle Swarm optimization(PSO) algorithm combined with multi-agent scheme is proposed for the thrust allocation in this paper. The algorithm which uses multi-agent topological structure has three functions that keeps the diversity of the particle swarm population, improving particle swarm global search ability, and enhancing information diversity. Relying on chaotic local search to get rid of local optima, it can also improve the convergence precision. The numerical simulations are conducted to demonstrate the effectiveness of the proposed methods, and the results are compared with PSO algorithm.

关键词： Dynamic positioning optimization algorithm thrust allocation Multi-Agent CPSO

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Lifetime optimization algorithm with Multiple Mobile Sink Nodes for Wireless Sensor Networks 8th

Lifetime Optimization Algorithm with Multiple Mobile Sink No...

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8th China Wireless Sensor Network Conference (CWSN)

作者： Chen, Yourong Chen, Di Liu, Yaolin Wang, Zhangquan Ren, Tiaojuan Zhejiang Shuren Univ Coll Informat Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783662469811;9783662469804

In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor networks (LOA_MMSN) is proposed. LOA_MMSN analyzes the constraints, establishes network optimization model, and decomposes the model into movement path selection model and lifetime optimization model with known movement paths. Finally, the two models are solved. Simulation results show that LOA_MMSN can extend the network lifetime, balance node energy consumption and reduce data gathering latency. Under certain conditions, it outper forms Ratio_w, TPGF and lifetime optimization algorithm with single mobile sink node for WSNs.

关键词： Network lifetime Mobile sink nodes optimization algorithm Sub-gradient method

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