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International Conference on Mechatronics and Control (ICMC)

作者： Guo, Mengxu Chen, Mou Zhang, Hangyue Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479925384

A nonlinear control allocation scheme is developed using hybrid optimization algorithm. To achieve nonlinear control allocation results, a hybrid optimization algorithm is presented in which the ant colony algorithm and differential evolution algorithm are employed. The nonlinear control allocation problem is divided into two optimal problems which are selecting optimal truncation point combination problem and optimizing the corresponding section coefficients problem. Under this case, the actuators constraints are segmented into some intervals according to the rate limits and position boundary of actuators. The optimal combination of cutoff interval is given through ant colony algorithm searching, and the optimal combination of truncation points is obtained. On the basis of the optimal truncation points, the corresponding truncation point coefficients are optimized by using the differential evolution algorithm. Then, the actuator commands are obtained by the calculation results of truncation points and corresponding truncation point coefficients. Simulation results show that the developed control allocation method is effective and the control requirement can be achieved.

关键词： nonlinear control allocation ant colony algorithm differential evolution algorithm hybrid optimization algorithm

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A novel lightweight combinatorial optimization strategy based on ASA-NLPQL optimization algorithm for front seat skeleton of a passenger car

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ADVANCES IN ENGINEERING SOFTWARE 2025年 205卷

作者： Zhou, Xuan Ju, Jun Long, Jiangqi Wenzhou Univ Coll Mech & Elect Engn Chashan St Wenzhou 325035 Peoples R China

This study presents a novel combinatorial optimization strategy to address the challenges of lightweight design for automotive seat skeletons. The proposed approach integrates surrogate modeling with a hybrid optimization algorithm to enhance computational efficiency and solution accuracy. The hybrid algorithm leverages the strengths of global and gradient-based optimization methods by combining Adaptive Simulated Annealing and Non-Linear Programming by Quadratic Lagrangian (ASA-NLPQL). The effectiveness of the ASA-NLPQL hybrid algorithm is validated through two numerical function examples and an engineering optimization case study, and it is subsequently applied to a real-world case study focused on the lightweight optimization design of an automotive seat skeleton. Firstly, the finite element model for modal analysis of the entire front seat skeleton of a passenger car is developed and validated through experimental tests. Key design variables are identified via sensitivity analysis to guide the optimization process. Subsequently, a surrogate model is constructed using sample points generated by optimal Latin hypercube sampling. The combined optimization strategy, based on the surrogate model and hybrid algorithm, is then applied to the optimal design of the passenger car seat skeleton. optimization results demonstrate a 22.3% reduction in seat weight with negligible impact on vibration performance. Finally, the optimized seat skeleton undergoes various strength tests, with the results confirming that it meets the required strength criteria. These findings demonstrate the effectiveness of the proposed optimization strategy and provide a reliable reference for similar engineering optimization challenges.

关键词： Automotive seat skeleton hybrid optimization algorithm Combinatorial optimization strategy Lightweight optimization design Surrogate model

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Dynamic optimization can effectively improve the accuracy of reference evapotranspiration in southern China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Xiao, Xiang Xiao, Ziniu Liu, Xiaogang Gu, Xinqin Di, Yanfeng Wu, Lifeng Zhang, Zexi Lv, Mingli Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Yunnan Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmospher Sci & Geoph Beijing Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing Peoples R China Hohai Univ Coll Agr Sci & Engn Nanjing 210098 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Key Lab Ecol Safety & Sustainable Dev Arid Lands Urumqi Peoples R China Shanghai Huawei Controllable Agr Technol Grp Co LT Shanghai Peoples R China

Rapid advancements in numerical simulation have endowed machine learning with abundant input features. Dynamic optimization of hyperparameters and feature combinations effectively captures intricate meteorological interactions, which is crucial for precisely estimating reference evapotranspiration (ET0). Utilizing NOAH data from the Global Land Data Assimilation System (GLDAS), we assessed the simulation accuracy of daily meteorological variables and ET0 across 16 stations in southern China. We introduced a hybrid model (HM) amalgamating Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), and Extreme Gradient Boosting (XGBoost). Our findings reveal that HM's GLDAS-NOAH outputs surpass those from Inverse Distance Weighting (IDW) and Convolutional Neural Networks (CNN), reducing average RMSEs for Tmax, Tmin, RH, Wind, and Rs by 32.6%, 47.4%, 43.3%, 38.7%, and 27.3%, respectively, and by 31.1%, 29.3%,-3.3%, 5.6%, and 19.0%, culminating in average ET0 RMSE reductions of 30.8% and 14.1%. Tmax exhibited the highest accuracy, followed by RH, Tmin, Rs, and Wind, with mean NRMSEs of 0.08 (0.04-0.24), 0.10 (0.05-0.14), 0.19 (0.03-3.00), 0.21 (0.14-0.29), and 0.35 (0.27-0.48). The mean NRMSE for ET0 was 0.21 (0.16-0.31). Excluding RH, HM's outputs reduced ET0 errors more effectively than IDW and CNN. RH contributed most to ET0 enhancement, followed by Rs, Tmax, Wind, and Tmin, with average NRMSE reductions of 13.24%, 10.07%, 3.28%, 2.75%, and 2.32%. HM's feature input combinations ranged from 6 to 10, most frequently 7. RH incorporated the most input features, averaging 8.6 per station. Equivalent variables emerged as the most significant features for Tmax, RH, Rs, and Wind, with average Gain values of 0.723, 0.456, 0.749, and 0.376, respectively. For Tmin, soil temperature was the predominant contributor, with an average Gain of 0.884. These results underscore that optimizing input feature combinations and hyperparameters in HM markedly enhances the simulation ac

关键词： hybrid optimization algorithm Feature importance analysis Dynamic optimization hybrid model Reference evapotranspiration Machine Learning

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Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning

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COMPUTERS & CHEMICAL ENGINEERING 2025年 192卷

作者： He, Renchu Xie, Yunhao Zhang, Shiwei Xu, Feng Long, Jian China Univ Petr Coll Artificial Intelligence Dept Automat Beijing 102249 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization *** integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits;short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

关键词： Crude oil scheduling Knowledge assisted Integrated planning and scheduling hybrid optimization algorithm

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Double-Loop-optimization-Based Joint Parameter Tuning for Dynamic Matrix Control With Validation in Intelligent Cigarette Drying Process

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7303-7315页

作者： Zhang, Yang Wang, Peng Li, Ning Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Dynamic matrix control (DMC) is widely used in intelligent manufacturing. Its performance is heavily affected by coupled parameters, e.g., horizons and weighting matrices. However, existing methods only focus on tuning of single type of parameter and cannot jointly tune them to improve the performance of DMC comprehensively. To bridge the gap, we propose a framework to tune DMC parameters based on double-loop-optimization jointly. The horizons of prediction and control are tuned in the outer loop, while the weighting matrices of error and control are tuned in the inner loop. An improved genetic algorithm fused with the particle swarm optimization is developed to minimize the cost function considering the constraints on horizons and weighting matrices. In the developed algorithm, population evolution and individual updating are deeply integrated to improve global optimality. The efficacy of the proposed framework is verified with real data of moisture control in cigarette drying process, which improves the quality of the moisture control by stabilizing the production equipment and reducing energy consumption.

关键词： Monte Carlo methods Tuning Moisture control Statistics Sociology Predictive models Heuristic algorithms Double-loop-optimization (DLO) framework dynamic matrix control (DMC) hybrid optimization algorithm intelligent moisture control joint tuning

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A hybrid optimization algorithm for Pairwise Test Suite Generation

A Hybrid Optimization Algorithm for Pairwise Test Suite Gene...

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IEEE International Conference on Information and Automation

作者： Rongzhi Qi Zhijian Wang Ping Ping Shuiyan Li College of Computer and Information Hohai University College of Science Hohai University

ISBN: (纸本)9781467391054

Pairwise testing is an effective combinatorial test generation technique that can generate relative small test suite to cover all pairs of parameter values at least once. Genetic algorithm has been used for pairwise test suite generation by some researchers. In order to improve the performance of genetic algorithm, this paper proposes a hybrid optimization algorithm by augmenting genetic algorithm with two-stage hill climbing. The first stage is to improve all the individuals after genetic operations. The second stage is to improve the best solution of the current generation at the end of each generation. A series of experiments are conducted to evaluate the proposed algorithm. Experiment results show that the proposed algorithm is very competitive with respect to other approaches reported in the literature.

关键词： Pairwise testing hybrid optimization algorithm Genetic algorithm Hill climbing

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Research on optimization method for flyby observation mission adapted to satellite on-board computation

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2024年第6期238卷 629-644页

作者： Wang, Jiyuan Xiao, Yan Ye, Dong Wang, Zijun Sun, Zhaowei Harbin Inst Technol Res Ctr Satellite Technol Mailbox 30132 Yi Kuang St Harbin 150001 Peoples R China Shanghai Satellite Engn Inst Dept Remote Sensing Satellite Shanghai Peoples R China

In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.

关键词： Orbital observation optimal orbital transfer hybrid optimization algorithm sequential quadratic programming depth-first search

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Modeling and optimization of a stand-alone desalination plant powered by solar/wind energies based on back-up systems using a hybrid algorithm

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ENERGY 2022年第PartC期254卷

作者： Zhang, Weiping Maleki, Akbar Northwestern Polytech Univ Sch Software Xian Peoples R China Zhejiang Univ Binhai Ind Technol Res Inst Hangzhou Peoples R China Shahrood Univ Technol Fac Mech Engn Shahrood Iran

An autonomous reverse osmosis desalination plant powered by photovoltaic, wind turbines, diesel generators, and energy storage systems is considered as one of the effective ways to solve the difficulty of supplying potable water and electricity demands in remote areas. Nevertheless, modeling and achieving optimal size from an economic, environmental, and reliability point of view is necessary. In this study, four stand-alone hybrid desalination plants driven by renewable energy and back-up systems are designed and modeled to solve the difficulty of supplying potable water and electricity demands in remote areas. The hybrid structure consists of photovoltaic, wind turbines, diesel generators, and energy storage systems, which is optimized based on the minimum total cost and damage to the environment and suitable reliability. The optimization problem is solved by the hybrid optimization method based on two meta-heuristic methods. To ensure the superiority of the proposed optimization method, the obtained results are compared with two well-known algorithms. To verify the model, Davarzan Town, located in Iran, is selected as a case study. The effects of reliability indexes on optimal sizing of different hybrid systems are compared in detail. The results reveal that the suggested optimization method achieves the best system design. Also, it is seen that the stand-alone desalination plant powered by solar/ battery storage/diesel generator structure in different unreliability index is cheaper and cleaner than the other proposed systems.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Stand-alone hybrid renewable energy systems Reverse osmosis desalination Solar wind energy Back-up system hybrid optimization algorithm

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Reactive Power optimization Model of Active Distribution Network with New Energy and Electric Vehicles

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Energy Engineering 2025年第3期122卷 985-1003页

作者： Chenxu Wang Jing Bian Rui Yuan Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control&Renewable Energy TechnologyMinistry of EducationJilin132011China

Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is ***,the k-medoids clustering algorithm is used to divide the reduced power scene into ***,the discrete variables and continuous variables are optimized in the same period of ***,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging *** to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution *** simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.

关键词： Active distribution network new energy electric vehicles dynamic reactive power optimization kmedoids clustering hybrid optimization algorithm

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Scheduling of combined heat and generation outputs in power systems using a new hybrid multi-objective optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10741-10757页

作者： Dolatabadi, Soheil El-Sehiemy, Ragab A. GhassemZadeh, Saeid Univ Tabriz Elect & Comp Engn Dept Tabriz Iran Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr El Shaikh Egypt

In this paper, a hybrid optimization algorithm, consisted of weighted vertices-based optimizer (WVO) and particle swarm optimization (PSO) algorithm, is proposed to solve three economic frameworks for scheduling of power sources in order to meet the required power demand in power systems. These frameworks are economic power dispatch, economic emission power dispatch and combined heat and economic power dispatch problems. The basic idea of weighted vertices optimizer (WVO) is given from the bisection root-finding method in mathematics. It uses swarm intelligence and evolutionary strategy to efficiently find the optimum solution. However, the original WVO algorithm has some flaws in complex problems with a high number of variables and constraints. Therefore, this paper presents hybrid WVO-PSO algorithm which solved the mentioned flaws and also improved its speed and accuracy. In this algorithm, varying speed is defined for each vertex by using PSO which helps better exploration through the search space. To evaluate the performance of WVO-PSO, it is applied to some of well-known and complex emission/economic dispatch (EED), combined heat and power economic dispatch (CHPED) and combined heat and power emission/economic dispatch (CHPEED) problems and then the driven results are compared with other recent methods which demonstrates better performance of the proposed method in solving non-convex and constrained EED, CHPED and CHPEED problem in terms of minimizing costs and emissions.

关键词： Emission and economic dispatch Combined heat and power unit Multi-objective optimization Weighted vertices optimizer hybrid optimization algorithm

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