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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Gao, Xiaoshan Yan, Liang Zhao, Peiran Du, Nannan Bu, Suwan Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Ningbo Inst Technol Ningbo 315800 Peoples R China

ISBN: (纸本)9781665409841

The inverse kinematics solution of the multi-DOF mobile manipulator is developed in the paper. The differential evolution (DE) algorithm is employed for solving the inverse kinematics of the mobile manipulator with 2P7R configuration due to its main advantages of fast convergence, few control parameters, good robustness, etc. The weighted value of pose error of the end effector and the position error of each joint configuration is used as the fitness function and the motion limit of each joint configuration is used as the constraint condition. Numerical simulations arc pertbrmed to test the performance in the inverse kinematics solution for the mobile manipulator with 21171( configuration, which indicates that the DE algorithm can maintain the smaller position and pose error than the other intelligent algorithm.

关键词： mobile manipulator inverse kinematics differential evolution algorithm

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Research and comparison of Doppler frequency processing algorithms for Jupiter spacecraft 41

Research and comparison of Doppler frequency processing algo...

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第41届中国控制会议

作者： HE Yongtao CHEN Congyan JIAN Nianchuan School of Automation Southeast University Shanghai Astronomical Observatory Chinese Academy of Sciences

ISBN: (纸本)9789887581536

Doppler frequency processing is of great significance in the field of deep space exploration,but the traditional processing algorithm based on phase-locked loop algorithm sometimes loses phase lock when the frequency changes too *** this paper,the Doppler processing algorithm based on differential evolution is used to process the collected frequency data of Jupiter probe Juno and evaluate its *** a comparison,the Juno orbit tracking data released by the Planetary Data System is decoded and the accuracy is also *** comparison,it is known that the accuracy evaluation results of the two are at the same level(about 18 mHz,Is integral),which shows that the Doppler data processing algorithm proposed in the paper is ***;this paper also briefly introduces Juno's ramped Doppler mode,analyzes the Doppler broadening phenomenon in the observation test,and puts forward the solutions.

关键词： Juno CDAS Doppler frequency differential evolution algorithm

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Multi-Objective evolutionary Optimization of Multi-node Network for Thermal Modelling of Electronic Package 28

Multi-Objective Evolutionary Optimization of Multi-node Netw...

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28th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)

作者： Eric, Monier-Vinard Najib, Laraqi ANSYS France Elect Thermal Management Montigny Le Bretonneux France Univ Paris Nanterre Lab Therm Interfaces Environm Ville Davray France

ISBN: (数字)9781665492294

ISBN: (纸本)9781665492294

In 1996, the concept of Compact Thermal Model (CTM) was proposed by the European research project referred to as DELPHI. Its objective was to from a set of data, generated by numerical simulations, to create the simplest multi-node thermal model that allows preserving an acceptable accuracy whatever the operating conditions of the inputs. The established model is a black-box object combined to a network of resistors that links a single temperature-sensitive node to major surfaces of heat extraction. This surrogate model is built with the aim of approximating the thermal behavior of an electronic component submitted to a large range of boundary conditions. Over the last two decades, new Reduced Order Model (ROM) methods were studied but at board modeling level, nodal analysis model remains the most practical solution to minimize numerical model size and computation times. However, the agreement of DELPHI's CTM standardized method suffers many limitations such as the choice of appropriate optimization techniques or the definition of training multi-objective criteria. The present work discusses the use of differential evolution (DE) algorithms to formulate a robust chromosomes-genes fitting procedure where a relevant multi-node network can be extracted. So, the performances of the Classic-DE algorithm were analyzed on several test cases of an electronic component family, referred to as Quad Flat No-lead package (QFN). Whatever the studied package size, a deduced six-node matrix proves its ability for training data to yield high-accuracy resistance-network models and to perform well for training-independent validation scenarios of boundary conditions. The prediction of the component most sensitive temperature using a very simple black-box model form never exceeds on average 1%.

关键词： electronic component nodal thermal modelling multi-node network differential evolution algorithm

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A scenario-based robust optimization with a pessimistic approach for nurse rostering problem

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2021年第1期41卷 143-169页

作者： Hassani, Mohammad Reza Behnamian, J. Bu Ali Sina Univ Dept Ind Engn Fac Engn Hamadan Hamadan Iran

Nurse rostering problem (NRP) or nurse scheduling problem is a combinatorial optimization problem that involves the assignment of shifts to nurses while managing coverage constraints, expertise categories, labor legislation, contractual agreements, personal preferences, etc. The focus on this problem serves to improve service quality, nurse health and their satisfaction, and reduction of hospital costs. The existence of uncertainties and inaccurate estimates of the workload leads to a non-optimal or an infeasible solution. In this study, due to the importance of human resource management and crisis management in the health care system, a sustainable approach was developed with a robust scenario-based optimization method. Since NRP is a NP-hard problem, it is impossible to solve it in medium and large sizes in reasonable time. In this paper, a well-known metaheuristic algorithm, namely the differential evolution (DE) algorithm was proposed due to its sound structural features for searching in binary space. Then its performance was compared against the genetic algorithm. The results show that the DE algorithm has good performance.

关键词： Nurse rostering problem Scenario-based approach Robust optimization differential evolution algorithm

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Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference evolution algorithm

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1177-1199页

作者： Annan Jiang Xinping Guo Shuai Zheng Mengfei Xu Highway and Bridge Institute Dalian Maritime UniversityDalian116026China

Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel *** ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time *** solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding *** calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following ***,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and ***,the GP regress model optimized by DE is trained by the data ***,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and *** proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,*** obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is *** results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is *** algorithm influencing factors are also discussed,and it is found that the local correlation

关键词： Gauss process regression differential evolution algorithm ubiquitous-joint model parameter identification orthogonal design

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Neural network-based hybrid models developed for free radical polymerization of styrene

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POLYMER ENGINEERING AND SCIENCE 2021年第3期61卷 716-730页

作者： Ghiba, Luciana Dragoi, Elena Niculina Curteanu, Silvia Gheorghe Asachi Tech Univ Fac Chem Engn & Environm Protect Cristofor Simion Bld Prof Dr Doc Dimitrie Mangeron 71A Iasi 700050 Romania

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results-predicted conversion and molecular weights as function of time, temperature, and initiator concentration-different combinations between phenomenological model and neural network are tested;also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.

关键词： artificial neural networks differential evolution algorithm free radical polymerization hybrid models modeling and simulation

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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Yameen, Muhammad Zubair Lu, Zhigang El-Sousy, Fayez F. M. Younis, Waqar Zardari, Baqar Ali Junejo, Abdul Khalique Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Key Lab Power Elect Energy Conservat & Drive Contr Qinhuangdao 066004 Peoples R China Prince Sattam bin Abdulaziz Univ Coll Engn Dept Elect Engn Al Kharj 16273 Saudi Arabia Quaid e Awam Univ Nawabshah Dept Informat Technol Nawabshah Pakistan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan Peoples R China

The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. This research presents an Adaptive Model Predictive Control (AMPC) framework to enhance GFM performance in Virtual Synchronous Machine (VSM) mode, ensuring robust frequency stability under uncertainties. The primary issue addressed is the inefficiency of traditional MPC in adapting to dynamic grid conditions. To resolve this, the AMPC framework combines offline reinforcement learning for parameter tuning with online MPC using soft constraints. The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive differential evolution algorithm (COA-jDE) to minimize the cost function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U_{offline}$$\end{document}, deriving optimal control parameters (Q, R) before real-time deployment. This process, termed cost function minimization using COA-jDE in a reinforcement learning framework, enhances GFM performance by adaptively adjusting virtual inertia and damping. Simulations on a 16MW wind-powered DFIG microgrid demonstrate that AMPC outperforms traditional MPC and VSM methods during grid disturbances, symmetrical faults, islanding, and load shifts. Furthermore, AMPC is computationally efficient compared to conventional reinforcement learning techniques, as adaptation is restricted to offline tuning. The framework not only improves compliance with grid codes (e.g., GC0137, IEEE 1547) but also provides a flexible, resilient control strategy for modern low-inertia grids.

关键词： Model Predictive Control Crayfish Optimization algorithm Microgrid Frequency DFIG Virtual Synchronous Machine differential evolution algorithm

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Collision avoidance path planning in multi-ship encounter situations

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2021年第4期26卷 1026-1037页

作者： Kang, Yu-Tao Chen, Wei-Jiong Zhu, Da-Qi Wang, Jin-Hui Shanghai Maritime Univ Coll Environm & Engn Shanghai Peoples R China Shanghai Maritime Univ Lab Underwater Vehicles & Intelligent Syst Shanghai Peoples R China

Collision avoidance path planning is still one of the essential problems in the design and application of an intelligent maritime navigation system. Its main obstacle is how to determine effective and cooperative collision avoidance maneuvers within a multi-ship encounter situation. By deconstructing a multi-ship encounter, this study adopted ship domain around target ships to assess the collision danger that own ship should avoid. Subsequently, the fitness function that has multiple dynamic obstacle constraints was designed in a two-dimensional map. Based on DE algorithm, a path-planning method was developed to compute collision-free and optimal navigation paths for ships. Simulation results show that the algorithm can generate a safe and suitable path from each perspective in a multi-ship encounter. The results also validate the practicality of the generated paths, consistency of the algorithm outputs and performance of the algorithm. It would be expected to provide a reference for collision avoidance decision making as well as contribute to the development of autonomous navigation systems.

关键词： Collision avoidance Ship domain Path planning differential evolution algorithm Multi-ship encounter

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UAV Swarm Trajectory and Cooperative Beamforming Design in Double-IRS Assisted Wireless Communications 18

UAV Swarm Trajectory and Cooperative Beamforming Design in D...

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18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Liao, Yangzhe Xia, Shuang Zhang, Ke Zhai, Xiaojun Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781665464574

Non-terrestrial communications have emerged as a technological enabler for seamless connectivity and ubiquitous computation services in the upcoming beyond fifth generation (B5G) and sixth generation (6G) networks. However, there exist numerous practical technical limitations, such as high deployment cost, massive energy consumption, high probability of information transmission blockage and dynamic propagation environments and so forth. Thanks to the rapid developments of meta-materials, the cost-effective and energy-efficiency intelligent reconfigurable surface (IRS) has been globally recognized as a revolutionized technology to construct smart radio environments. In this paper, a novel double-IRS assisted unmanned aerial vehicles (UAV)-swarm-enabled communication network architecture is proposed, where two UAV swarms are integrated with the main IRS reflector and subreflector, respectively. The energy minimization problem of UAV swarm carried main IRS is formulated, subject to a list of quality of service (QoS) constraints. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where the enhanced differential evolution (DE) algorithm is proposed to optimize the UAV swarm trajectory and the alternate optimization algorithm is utilized to optimize the cooperative reflect beamforming vector. Numerical results validate that the proposed algorithm outperforms several selected advanced algorithms regarding UAV swarm energy consumption. Moreover, the network performance under the different number of IRS elements is investigated.

关键词： Intelligent Reconfigurable Surface Unmanned Aerial Vehicle QoS Energy Minimization differential evolution algorithm

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