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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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STSA: A sine Tree-Seed algorithm for complex continuous optimization problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 537卷 122802-000页

作者： Jiang, Jianhua Xu, Meirong Meng, Xianqiu Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Tree-Seed algorithm (TSA) has good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. This paper makes an intensive analysis of TSA. In order to keep the balance between exploration and exploitation, we propose an adaptive automatic adjustment mechanism. The number of seeds can be defined in the initialization process of the optimization algorithm. In order to further improve the convergence rate of TSA, we also modify the change model of seed numbers in the initialization process with randomly changing from more to less. With the improvement of two mechanisms, the main weakness of TSA has been overcome effectively. Based on the above two improvements, we propose a new algorithm-Sine Tree-Seed algorithm (STSA). STSA achieves good results in solving high-dimensional complex optimization problems. The results obtained from 24 benchmark functions confirm the excellent performance of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Tree-Seed algorithm Sine Cosine algorithm Continuous optimization optimization algorithm

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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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Enhanced slime mould algorithm with multiple mutation strategy and restart mechanism for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5069-5083页

作者： Zheng, Rong Jia, Heming Wang, Shuang Liu, Qingxin Sanming Univ Coll Informat Engn Sanming Fujian Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China

Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in local optimum at times. Therefore, the enhanced SMA named as ESMA is presented in this paper for solving global optimization problems. Two effective methods composed of multiple mutation strategy (MMS) and restart mechanism (RM) are embedded into the original SMA. MMS is utilized to increase the population diversity, and the RM is used to avoid the local optimum. To verify the ESMA's performance, twenty-three classical benchmark functions are employed, as well as three well-known engineering design problems, including welded beam design, pressure vessel design and speed reducer design. Several famous optimization algorithms are also chosen for comparison. Experimental results show that the ESMA outperforms other optimization algorithms in most of the test functions with faster convergence speed and higher solution accuracy, which indicates the merits of proposed ESMA. The results of Wilcoxon signed-rank test also reveal that ESMA is significant superior to other comparative optimization algorithms. Moreover, the results of three constrained engineering design problems demonstrate that ESMA is better than comparative algorithms.

关键词： Slime mould algorithm multiple mutation strategy restart mechanism global optimization optimization algorithm

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Heuristic and random search algorithm in optimization of route planning for Robot's geomagnetic navigation

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COMPUTER COMMUNICATIONS 2020年第0期154卷 12-17页

作者： Xu, Yan Guan, Guofei Song, Qingwu Jiang, Chao Wang, Lihui Jiangsu Frontier Elect Technol Co Ltd Nanjing 211102 Peoples R China Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Techno Minist Educ Nanjing 210096 Peoples R China

To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.

关键词： Geomagnetic field navigation Route planning Geomagnetic navigation matching area Environmental constraint modeling optimization algorithm

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A Novel Software Tool for Automated and Integrated Building Energy Model Calibration 11th

A Novel Software Tool for Automated and Integrated Building ...

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11th International Conference of Ar.Tec.

作者： Maracchini, Gianluca D'Orazio, Marco Di Giuseppe, Elisa Revel, Gian Marco Univ Trento Dept Civil Environm & Mech Engn I-38123 Trento Italy Marche Polytech Univ Dept Construct Civil Engn & Architecture I-60131 Ancona Italy Marche Polytech Univ Dept Ind Engn & Math Sci I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

In the EU, the building sector significantly impacts energy consumption and greenhouse gas emissions, accounting for 40% of the total energy use and 35% of emissions, mainly due to the energy inefficiency of the building stock. With energy demand expected to increase over the next decade, improving building energy efficiency is essential for meeting EU sustainability goals. Building Energy Models (BEMs) are crucial for evaluating and enhancing building performance throughout their lifecycle. However, a notable "energy performance gap" usually exists between predicted and actual energy use, exacerbated by challenges in accurately inputting numerous variables and the simplifications inherent in modeling. BEM calibration (BC) approaches are often adopted to reduce these discrepancies, aimed at adjusting model inputs to match output with the observed data. Yet, there is not a universal consensus on which is the best calibration method, with manual and automated approaches offering different benefits. Automated methods, especially those using optimization algorithms, have gained prominence for their efficiency and ability to handle uncertainties. However, BC still significantly depends on the energy modelers' expertise. This paper introduces a novel software tool for automated BC, aiming to simplify the process by integrating expert knowledge, sensitivity analysis, and optimization algorithms techniques in a unique workflow. This tool reduces the dependence of BC success on modeler expertise, representing a significant step towards more accessible automated BC in the research field and engineering practice, thence allowing a more effective design of energy conservation measures.

关键词： Building Energy Model Model calibration optimization algorithm Sensitivity analysis Software

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Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第2期10卷 1-12页

作者： Mohebian, Pooya Motahari, Mohammad Reza Aval, Seyed Bahram Beheshti Arak Univ Fac Engn Dept Civil Engn Arak Iran KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Bridges are one of the most pivotal civil engineering structures. Nevertheless, they are highly susceptible to localized damage, which poses a gradual threat to their long-term stability and functionality. Hence, it is of utmost importance to detect any damage in bridges to ensure their continued safe and reliable performance. In view of this, the present study proposes an optimization-based finite element model updating method to perform arch bridge damage detection by using vibration data and the gradient-based optimizer (GBO) algorithm. The damage severity of structural elements in different parts of the bridge, including those in the arch, columns, and deck, are taken as damage variables. The objective function of the optimization problem is also defined in terms of discrepancies between the vibration data of the actual damaged bridge and those computed from the finite element model. The GBO algorithm, as a new robust meta-heuristic algorithm, is applied to tackle the damage detection problem, and its performance is compared to other well-known meta-heuristic algorithms. The applicability of the proposed method is numerically assessed on an arch bridge structure under both noise-free and noisy conditions. The obtained results demonstrate the accuracy and computational efficiency of the presented method in dealing with the arch bridge damage detection problem.

关键词： Damage detection Arch bridge Vibration method Finite element model updating optimization algorithm

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Turboprop Cycle optimization Using Repulsive Particle Swarm algorithm

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JOURNAL OF PROPULSION AND POWER 2010年第4期26卷 882-891页

作者： Boulkeraa, Tayeb Ghenaiet, Adel EMP Lab Thermal Power Syst Appl Mech Algiers 16111 Algeria

A numerical approach to select an optimized turboprop engine configuration that matches with the power requirement of a class of propeller-driven aircraft, was reported. Biobjective optimizations of the propulsion cycles' parameters for three variants of turboprops were carried out based on an efficient repulsive particle swarm algorithm (RPSO). On each Pareto front, three distinguishable design points were selected. A cycle optimization was performed considering a basic technology level to determine the optimum turbine inlet temperature (TIT), overall pressure ratio (OPR), and turbine temperature expansion rate (TTR) and the subsequent performance for the three configurations of turboprops. The optimizations carried out for the three turboprops' configurations at the same cruising conditions showed that for a basic technology level the two-shaft fixed-turbine turboprop is preferred, owing to better performance for the same OPR range, but needs more LP turbine stages as compared with the free-turbine version.

关键词： Turboprop Engine Evolutionary algorithm Power Specific Fuel Consumption Turbine Inlet Temperature Fuel Consumption Mass Flow Temperature Ratio Numerical Modeling Exhaust Nozzles optimization algorithm

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