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optimization algorithm based on kinetic-molecular theory

Journal of Central South University 2013年第12期20卷 3504-3512页

作者：范朝冬欧阳红林张英杰艾朝阳 College of Electrical and Information Engineering Hunan University College of Information Science and Engineering Hunan University Institute of Cognitive Science Hunan University

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

关键词： optimization algorithm heuristic search algorithm kinetic-molecular theory diversity convergence

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Optimal Design of Terminal Sliding Mode Controller for Direct Torque Control of SRMs

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2022年第1期8卷 1445-1453页

作者： Sun, Xiaodong Feng, Liyun Zhu, Zhen Lei, Gang Diao, Kaikai Guo, Youguang Zhu, Jianguo Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Univ Technol Sydney Sch Elect & Informat Engn Sydney NSW 2007 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

A nonsingular terminal sliding mode controller (NTSMC) based on a direct torque control is presented for a switched reluctance motor (SRM) in this article. To guarantee dynamic stability, the nonsingular terminal sliding mode based on an improved reaching law (RL) is employed to design the speed controller. The torque ripple of the system can be suppressed, and the disturbance caused by uncertainties, such as load disturbance and parameter perturbation, can be suppressed by the proposed NTSMC. Moreover, the gray wolf optimization algorithm is applied to automatically adjust the parameters of the controllers and the value of given flux, thereby acquiring a satisfactory result. The NTSMC is validated by both simulation and experimental results with a six-phase 12/10 SRM. Compared with PI and conventional sliding mode control, NTSMC improves the convergence rate of state and exhibits better performance in torque ripple reduction and antidisturbance ability. The robustness and dynamic performance of the system can be ensured.

关键词： Uncertainty Torque optimization Transportation Switches Convergence Torque control Direct torque control (DTC) optimization algorithm switched reluctance motor (SRM) terminal sliding mode control (SMC)

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Evolution of the shape parameters of photovoltaic module as a function of temperature and irradiance: New method of performance prediction without setting reference conditions

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ENERGY REPORTS 2022年 8卷 13859-13875页

作者： Lu, Hao Zhang, Yunpeng Hao, Peng Ma, Jiao Zhang, Li Gu, Tingkun Yang, Ming Shandong Univ Sch Elect Engn Jinan Peoples R China

The accurate characterization and prediction of current-voltage (I-V) characteristics of photovoltaic (PV) modules under different weather conditions are essential for solar power forecasting and ensuring grid stability. This paper proposed a novel method based on the power-law model (PLM) for estimating PV module performance under different weather conditions without setting reference conditions. The effects of solar irradiance, ambient temperature, and module types are all fully considered. The dependence of shape parameters in PLM on irradiance and temperature are thoroughly investigated and the effect of the selection of reference conditions is eliminated by modifying a set of new transforming equations. The parameters of the new transforming equations can be extracted from experimental data by an optimization algorithm. Due to the advantages of PLM, the proposed method applies to any type of PV module and the I-V characteristics can be expressed explicitly without Lambert W-function or iterative solution. The effectiveness and accuracy proposed method are verified and tested on the large datasets of eighteen PV modules in three locations. Compared with existing methods, the proposed method shows higher accuracy and better performance in the estimation of I-V characteristics and maximum power under different weather conditions.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Photovoltaic modules Performance estimation Power law model optimization algorithm

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A General and Automatic RMS Current Oriented Optimal Design Tool for LLC Resonant Converters

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第6期10卷 7318-7332页

作者： Wei, Yuqi Pereira, Thiago Pan, Yiwei Liserre, Marco Blaabjerg, Frede Mantooth, H. Alan Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Univ Kiel Chair Power Elect D-24118 Kiel Germany Aalborg Univ AAU Energy Dept DK-9220 Aalborg Denmark

LLC resonant converters are used in renewable energy applications to achieve high power efficiency conversions between different energy sources, buses, and energy storage elements. The traditional design methods for LLC resonant converters are based on a simple frequency-domain analysis (FDA). However, the accuracy of FDA is not satisfactory, especially at wide voltage range applications. To overcome the drawbacks of the traditional FDA method in wide-range applications, in this article, the time-domain analysis (TDA) is adopted to achieve accurate analysis of LLC converters. Efficiency is normally selected as the optimization objective, where the circuit components and accurate power loss models are required beforehand. To achieve a more general optimal design method for LLC converters, a simple root-mean-square (rms) current and TDA-based optimal design method is proposed. The optimal design is achieved by minimizing the converter rms current and ensuring some key design considerations. Moreover, an automatic design tool for the proposed rms-current-based optimal design method is developed, and advanced optimization algorithms are introduced to improve the optimization speed. A 2.5-kW experimental prototype is built using the optimal circuit parameters. Experimental results under different operating conditions are demonstrated, and the results are consistent with the theoretical analysis. Efficiency comparisons between the proposed optimal design method and conventional design method are made. The proposed optimal design method can improve the converter efficiency by a maximum value of 2.14%.

关键词： Automatic design optimization algorithm resonant converter root-mean-square (rms) current

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Guideline on Initial Point Finding and Search Bounds Setting for Biomedical Magnetic Localization

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IEEE SENSORS JOURNAL 2022年第16期22卷 16492-16504页

作者： Fu, Yuming Guo, Yongxin Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Natl Univ Singapore Chongqing Res Inst Ctr Intelligent Sensing & Artificial Intelligence Chongqing 401123 Peoples R China

Permanent magnet tracking technology provides a feasible way to localize noninvasive in vivo biomedical devices such as wireless capsule endoscopes. However, current permanent magnet tracking technology that typically relies on an optimization algorithm suffers from a trade-off between the accuracy, consistency, and speed of the results. In particular, in practical applications such as the magnetically actuated capsule, poor localization results are undesirable. In other words, high consistency and accuracy must be achieved concurrently. In this study, we propose an initial point finding method for optimization algorithms based on a semi-soft classifier that adopts a fully connected network and a convolution neural network as base classifiers. Furthermore, we present search bound setting guidelines for the optimization algorithms based on the estimated initial point. By setting tight bounds, the consistency and speed of the process can be improved while maintaining high accuracy. The proposed method is validated via simulations and experiments, and outperforms the previous methods under the same system configuration. Within an effective working space of 42 cm x 30 cm x 24 cm, it achieves an update rate of approximately 9-Hz, higher localization consistency, zero occurrence rate of poor localization results, an average error of 1.3 mm for position estimation, and an average error of 2.7 degrees for orientation estimation.

关键词： Convolutional neural network fully connected neural network magnetic localization optimization algorithm permanent magnet tracking system wireless capsule endoscope

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Multi-Objective optimization-Based Recommendation for Massive Online Learning Resources

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IEEE SENSORS JOURNAL 2021年第22期21卷 25274-25281页

作者： Li, Hui Zhong, Zhaoman Shi, Jun Li, Haining Zhang, Yong Jiangsu Ocean Univ Sch Comp Engn Lianyungang 222000 Peoples R China Jiangsu Prov Res & Dev Inst Marine Resources Lianyungang 222000 Peoples R China Ningxia Med Univ Dept Neurol Gen Hosp Yinchuan 750004 Ningxia Peoples R China

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the "information overload" problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary algorithm-based online learning Resource Recommendation Model to balance the system's accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm.

关键词： Sensors optimization Prediction algorithms Evolutionary computation Clustering algorithms Indexes Heating systems Multi-objective optimization optimization algorithm online learning resources learning resource recommendation

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Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm optimization in Wireless Sensor Networks: Forest Fire Detection

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期118卷 3501-3522页

作者： Vinodhini, R. Gomathy, C. SRM IST Elect & Commun Engn Vadapalani Campus Chennai Tamil Nadu India

Wireless sensor networks (WSNs) displays an encouraging outcome for forest fire (FF) identification. The most serious WSN investigation tasks is forest fire's early prediction, which is used to save the ecosystem. For conveying the detected information to the BS (base station), the SNs (sensor nodes) are placed in remote forest region in WSN based FF discovery scheme that is manageable by the forest sector. Various studies have been finished in this field but they studied only few amount of constraints and the encountered situations influence has not discussed after the system positioning. In this work, fuzzy based unequal clustering and context aware routing (CAR) procedure with GSO (glow-worm swarm optimization) is developed in RWP (random way point) based dynamic WSNs. Based on FL (fuzzy logic) the unequal clustering is formed and the optimal CH (cluster head) is nominated to convey the information from CM (cluster member) to BS to increase the system lifespan and to decrease the energy consumption. Further, the routing process is performed by the CAR procedure with GSO process to enhance the efficiency of network. Lastly, a case study of FF identification is offered as a justification of the suggested method. The suggested work is executed in MATLAB. The simulation outcomes proved that the proposed approach provide the better outcomes in average energy consumption (0.025 J), PDR (99.4%), jitter (4.01 s), delay (0.0304 s), BER (15%), throughput (144.6Kbps), network lifetime (38.7 s) as related to other current protocols.

关键词： Sensor networks optimization algorithm Routing protocol Context aware Fuzzy logic Fire detection in forest

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A Novel Optimal Layout Method of Airborne Distributed POS Based on MAC and GMC Hybrid optimization Criterion

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IEEE SENSORS JOURNAL 2021年第12期21卷 13638-13648页

作者： Gong, Xiaolin Sun, Lujin Peng, Zhiwei Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China

The layout of the airborne distributed position and orientation system (POS) directly affects the overall accuracy of the motion parameters of all load points obtained by the distributed POS. However, there are restrictions of space, weight and cost of the distributed POS, it is unrealistic to install an inertial measurement unit (IMU) at the location of each load. To solve this problem, based on modal analysis of wing structure, considering the two factors of the modal spatial intersection angle and the vibration amplitude, a novel fitness function model based on modal assurance criterion (MAC) and Gramian matrix criterion (GMC) is proposed to evaluate the population in the optimization algorithm. The hybrid adaptive particle swarm optimization and genetic algorithm (HAPSOGA) based on this proposed model is applied to the multiple system layout of airborne distributed POS to obtain the optimal layout scheme. The results of semi-physical simulation and ground verification experiment show that compared with the optimal method based on MAC, the optimal layout scheme determined by the proposed method can use less IMU and obtain higher overall accuracy of motion parameters for the remote sensing loads.

关键词： Layout optimization Strain Atmospheric modeling Vibrations Sensors Remote sensing Distributed position and orientation system SINS GPS integration layout optimization optimization algorithm optimization criterion

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Manufacturing Imperfections of Open Cross-Section Deployable Thin-Shell Composite Structures

Manufacturing Imperfections of Open Cross-Section Deployable...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Carmi, Meital O. Pellegrino, Sergio Graduate Aerospace Laboratories California Institute of Technology 1200 E California Blvd MC 105-50 PasadenaCA91125 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Characterizing the geometric imperfections of ultra-thin composite structures is important since imperfections create weak points where local buckling is likely to occur. This work develops a thorough method for measuring the geometric imperfections in thin-shell composite longerons. The mean geometrical parameters, the axial twist, and the lengths, amplitudes and locations of both local and periodic radial imperfections in experimental composite longerons are measured using this method. The results of this study identify the dominant imperfections commonly introduced during the manufacturing of these structures. This research was carried out with financial support from the National Science Foundation’s Graduate Research Fellowship Program and the Caltech Space Solar Power Project. The authors would like to acknowledge Professor Dan Meiron for his guidance on the wavelength decomposition used in this work. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Composite Structures Longerons Shape Functions Numerical Modeling Computing Autoclave Thin Shell Structure MATLAB optimization algorithm Structural Failure

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Metamodeling in Evolutionary Multi-Objective optimization for Constrained and Unconstrained Problems

Metamodeling in Evolutionary Multi-Objective Optimization fo...

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作者： Hussein, Rayan Michigan State University

学位级别：Ph.D., Doctor of Philosophy

One of the main difficulties in applying an optimization algorithm to a practical problem is that the evaluation of objectives and constraints often involve computationally expensive procedures. To handle such problems, a metamodel (or surrogate model, or response surface approximations) is first formed from a few exact (high-fidelity) solution evaluations, and then optimized by an algorithm in a progressive manner. However, there has been lukewarm interest in finding multiple trade-off solutions for multi-objective optimization problems using surrogate models. The literature on surrogate modeling for constrained optimization problems is also rare. The difficulty lies in the requirement of building and solving multiple surrogate models, one for each Pareto-optimal solution. In this study, we propose a taxonomy of different possible metamodeling frameworks for multi-objective optimization and provide a comparative study by discussing advantages and disadvantages of each framework. Also, we argue that it is more efficient to use different metamodeling frameworks at different stages of the optimization process. Thereafter, we propose a novel adaptive method for switching among different metamodeling frameworks. Moreover, we observe the convergence behavior of the proposed approaches is better with a trust regions method applied within the metamodeling frameworks. The results presented in this study are obtained using the well-known Kriging metamodeling approach. Based on our extensive simulation studies on proposed frameworks, we report new and interesting observations about the behavior of each metamodeling framework, which may provide salient guidelines for further studies in this emerging area within evolutionary multi-objective optimization. Results of this study clearly show the efficacy and efficiency of the proposed adaptive switching approach compared to three recently-proposed other metamodeling algorithms on challenging multi-objective optimization problems u

关键词： Metamodeling optimization algorithm Surrogate models

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