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A hybrid TCNN optimization Approach for the Capacity Vehicle Routing Problem

Journal of Shanghai Jiaotong university(Science) 2006年第1期11卷 34-39页

作者：孙华丽谢剑英薛耀锋 Dept.of Automation Shanghai Jiaotong Univ.Shanghai 200030China

A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.

关键词： capacity vehicle routing problem fuzzy C-means transiently chaotic neural network hybrid optimization algorithm

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Adaptive protection coordination in microgrid based on nature inspired meta-heuristic optimization algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Kumari, Rani Naick, Bhukya Krishna Indian Inst Technol IIT Dept Elect Engn Dhanbad India

Ensuring a robust protection system is crucial for safeguarding the integrity of the overall system against abnormalities. Incorporating distributed generation (DG) into the distribution network can introduce fluctuations in fault current levels and directions, potentially causing mismatches in the response of the existing coordination system. This study proposes an adaptive protection coordination scheme designed to accommodate both grid-connected and standalone modes, addressing various fault scenarios. Utilizing a hybrid WCMFO algorithm, optimal relay settings are determined to facilitate effective coordination within a microgrid setup. The proposed method has been analyzed on 9 bus Canadian benchmark system integrated with four DGs. The performance of the proposed method is compared to other optimization techniques to demonstrate its effectiveness. System modelling is conducted using MATLAB/Simulink, and validation is further carried out using industrial ETAP software on a test microgrid system. The analysis extends to evaluating the enhancement in overall system reliability, quantified in terms of energy not supplied (ENS).

关键词： adaptive protection distributed generation DOCR hybrid optimization algorithm protection coordination

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hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

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SCIENTIFIC REPORTS 2024年第1期14卷 1-29页

作者： Mohammad, Saleem Jeebaseelan, S. D. Sundarsingh Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 India

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

关键词： hybrid optimization algorithm Sine-Cosine algorithm (SCA) Spotted Hyena Optimizer (SHO) Chimp optimization algorithm (ChOA) Proportional-integral (PI) controllers Microgrid Renewable energy integration Integral of Time-Weighted Squared Error (ITSE)

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Adjustable Robust optimization for Large-Scale Photovoltaics Planning in Smart Distribution Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年

作者： Ge, Leijiao Li, Yuanliang Yan, Jun Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin300072 China Jinan Power Supply Company State Grid Shandong Jinan250013 China Concordia University Concordia Institute for Information Systems Engineering MontréalQCH3G 1M8 Canada

High penetration in distributed photovoltaics (PVs) enhances the resilience of distribution network operations. However, it brings many uncertain issues such as the voltage and harmonic fluctuations that may affect the distribution network stability. Therefore, the determination of the PV location and capacity is of vital importance for smart distribution network planning. Considering the fluctuation of PV outputs and the robustness of optimization results, this paper proposes an adjustable robust optimization method for large-scale PV planning in smart distribution networks. The AC power flow is first converted into a convex program by the linearization theory, and the bi-level optimization is converted to a single-layer optimization problem using the strong duality theory. A hybrid algorithm based on the integration of grey wolf optimization (GWO) and improved particle swarm optimization (IPSO), named GWO-IPSO, is proposed to determine the robust optimal location and the capacity of distributed PVs. The proposed method is verified based on the IEEE 69-bus radial distribution network case study. The proposed method efficiently adapts to parameter uncertainties resulting from extreme scenarios and delivers better performance than general heuristic methods. © 2016 IEEE.

关键词： adjustable robust optimization bi-level optimization conversion Distributed photovoltaic planning hybrid optimization algorithm

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Parameter Estimation of Disk Drive Servo System Using A hybrid Simplex Differential Evolution algorithm

Parameter Estimation of Disk Drive Servo System Using A Hybr...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wang, Yaonan Wu, Lianghong Yuan, Xiaofang Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781424467129

To accurately maintain the head position along the center of the track, determining the optimal parameters of the disk drive servo control system is one of most important works. In this paper, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the differential evolution (DE) algorithm was presented to the optimum parameter estimation of a disk drive servo system. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Three experiments under the different conditions of command signal, sampling period and measuring noises were used to test the effectiveness of the proposed approach. The experiment results demonstrate that the hybrid optimization algorithm is superior to the two original search techniques (i.e. NM and DE) in terms of solution quality and convergence rate, and is an effective method for the parameter estimation of a control system.

关键词： Parameter estimation Disk drive servo system Differential evolution Simplex method hybrid optimization algorithm

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The Design Study on A Novel MW Wind Power Generation Speed-up Machine

The Design Study on A Novel MW Wind Power Generation Speed-u...

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3rd International Conference on Materials Science and Engineering (ICMSE 2014)

作者： Han, Lifen Ding, Weifeng Zhong, Shouyan Liao, Zilong Dongguan Univ Technol Coll Mech Engn Dongguan Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China

ISBN: (纸本)9783037859520

A novel efficient transmission, toroidal drive, was introduced into the field of wind power. A novel MW (Million Watt) wind power generation speed-up machine (WPGSM) was designed to replace the gear speed-up machine. The structure scheme of the MW WPGSM was designed and the design platform for the MW WPGSM was developed by UG/OPEN, VC++ and hybrid optimization algorithm based on the unigraphics (UG) software.

关键词： MW WPGSM toroidal drive parametric design and model parameter optimization hybrid optimization algorithm

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Jinliang A Novel Method for Mechanical Constitutive Parameters Estimation of Human Left Ventricle

Jinliang A Novel Method for Mechanical Constitutive Paramete...

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5th International Conference on Mechanical, Automotive and Materials Engineering (CMAME)

作者： Li, Yiqiang Cai, Li Huang, Zhiqiang Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China

ISBN: (纸本)9781538604328

In this paper, a new framework for estimation of myocardium constitutive parameters is established. A more realistic, cardiac magnetic resonance image based realistic human left ventricular finite element analysis model is introduced for analyzing the deformation of left ventricle during diastole firstly, the material behavior is described by the anisotropic nonlinear Holzapfel-Ogden constitutive model;then a novel hybrid simplex and quantum behavioral particle swarm optimization algorithm which is proposed to estimate the constitutive model parameters of myocardium as the inverse problem of left ventricle deformation. Numerical examples show that finite element analysis results and the estimated parameters are in good agreement with the experimental data reported in literature, which demonstrate that parameter estimation framework is valid. Comparing with current optimization algorithm, the presented hybrid optimal algorithm can estimate the mechanical parameters more efficiently.

关键词： hybrid optimization algorithm Nonlinear constitutive model Material parameter estimation

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Parameter Identification and Nonlinear Compensation Control Design of Electronic Throttle

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IFAC-PapersOnLine 2018年第31期51卷 435-441页

作者： Feng K. Sun B. Hu Y. Gao J. State Key Laboratory of Automotive simulation and control Nanling Campus Jilin University Changchun 130022 China Department of Control Science and Engineering Nanling Campus Jilin University Changchun 130022 China Department of Automotive Engineering Nanling Campus Jilin University Changchun 130022 China

Precise electronic throttle control is a basic problem of engine control. This paper identifies the model parameters based on input PWM, throttle output angle and angular velocity. But the angular velocity can not be measured directly. Therefore, the kalman filter is applied to calculate the angular velocity. A hybrid optimization algorithm is used to search the parameters of throttle model which minimizes the errors between model and real throttle output angle and angular velocity. The controller is designed based on identified parameters and triple-step nonlinear control design. Experimental results indicate the effectiveness of the identified parameters and the controller. © 2018

关键词： hybrid optimization algorithm parameter identification throttle control triple-step nonlinear control design

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Minimum-energy Optimal Control System for Four-wheeled Omni-directional Mobile Robot 3

Minimum-energy Optimal Control System for Four-wheeled Omni-...

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3rd International Symposium on Autonomous Systems (ISAS)

作者： Wang, Jianbin Chen, Jianping Bao, Pingmeng Zhaoqing Univ Sch Comp Sci & Software Zhaoqing Peoples R China Zhaoqing Univ Innovat & Entrepreneurship Coll Zhaoqing Peoples R China

ISBN: (纸本)9781728112985

The four-wheeled omnidirectional mobile robot driven by four electric motors is a typical redundant system. In this paper, a minimum-energy optimal control system for motion control of a four-wheeled omnidirectional mobile robot is presented. The kinematics of the mobile robot is built. And then a trajectory tracking controller is designed based on the kinematics model. Considering the wheel velocity redundancy, a harmony search and genetic algorithm hybrid optimization algorithm is applied to search for the best inputs by minimizing the motion energy. The designed control algorithm has been extensively tested in simulation. The obtained results indicate that the trajectory tracking control system has good stability and accuracy with minimum-energy consumption.

关键词： omni-directional mobile robot minimum-energy trajectory tracking hybrid optimization algorithm

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hybrid optimized using grey wolf-flower pollination for wireless sensor network routing

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International Journal of Information Technology (Singapore) 2023年第4期15卷 2263-2271页

作者： Ramya, R. Padmapriya, K. Department of Electronics and Communication Engineering Annamalai University Tamil Nadu Chidambaram India Department of Electronics and Communication Engineering Government College of Engineering (Deputed – Annamalai Univerity Chidambaram) Tamilnadu Tirunelveli India

Establishing energy efficient communication with minimum delay in wireless sensor networks (WSN) is a challenging task. Routing protocols are evolved to meet the WSN communication challenges. Optimal path selection for data transmission enhances the network lifetime and quality of services. Various optimization models have evolved so far for WSN routing. However, due to high convergence time and cost, there is a gap towards developing a better model to attain efficient routing. A hybrid optimization model using grey wolf optimization and flower pollination optimization algorithm is presented in this research work to select optimal route for data transmission. In the proposed network structure, the cluster heads are selected using Type 2 fuzzy logic and optimal paths are selected using a hybrid optimization algorithm considering the energy, delay, lifetime and delay parameters. The performance of proposed hybrid optimization is validated through simulation analysis and compared with existing optimization algorithms. The proposed routing model exhibits better performances attaining of minimum delay, energy consumption, and maximum throughput. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Cluster head selection Fuzzy logic hybrid optimization algorithm Routing Wireless sensor networks

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