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Optimization of dynamic buckling for sandwich nanocomposite plates with sensor and actuator layer based on sinusoidal-visco-piezoelasticity theories using grey wolf algorithm

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JOURNAL OF SANDWICH STRUCTURES & MATERIALS 2020年第1期22卷 3-27页

作者： Kolahchi, Reza Keshtegar, Behrooz Fakhar, Mohammad Hosein Islamic Azad Univ Dept Mech Engn Kashan Branch Kashan Iran Univ Zabol Fac Engn Dept Civil Engn Zabol Iran

Optimization of embedded piezoelectric sandwich nanocomposite plates for dynamic buckling analysis is presented in this work based on grey wolf algorithm. The grey wolf algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In addition, the main steps of hunting, searching for prey, encircling prey, and attacking prey are employed. The structure is composed of a laminated functionally graded-carbon nanotubes reinforced layers as core integrated with sensor and actuator layers considering structural damping effects. Two-dimensional magnetic and 3D electric fields are applied to core and piezoelectric layers, respectively. Sinusoidal shear deformation theory is utilized for obtaining the motion equations and differential quadrature method is applied for solution. Also, a proportional-derivative controller is employed to control the dynamic behavior of the structure. Finally, the optimum designs for the structure are evaluated using proposed grey wolf algorithm based on the geometrical parameters of plate, applied voltage, controller parameters, volume fraction of carbon nanotubes, spring, and shear constants of foundation. Numerical results indicate that by applying the positive voltage and transverse magnetic field the optimum dimensionless frequency of the system decreases.

关键词： Optimization dynamic buckling grey wolf algorithm functionally graded-carbon nanotube-reinforced sandwich plates sensor and actuator

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Parameter optimization of type II fuzzy sliding mode control for bridge crane systems based on improved grey wolf algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第5期45卷 2136-2152页

作者： Sun, Zhiqiang Sun, Zhe Xie, Xiangpeng Sun, Zhixin Nanjing Univ Posts & Telecommun Big Data Technol & Applicat Engn Res Ctr Jiangsu P Nanjing Peoples R China Nanjing Univ Posts & Telecommun Post Ind Technol Res & Dev Ctr State Posts Bur Internet Things Technol Nanjing 210023 Peoples R China

Bridge cranes are complex nonlinear dynamic systems with underactuated characteristics, making it challenging for controllers to man age the spatial swing of the load effectively. Additionally, uncertainties both within and outside the system adversely impact control performance. To address these issues, a Type-II fuzzy sliding mode controller has proven effective in enhancing the anti-swing control performance of the payload. However, due to the intricate parameter adjustment optimization problem and potential challenges in dealing with nonlinearity and uncertainty, especially in complex dynamic systems, this paper proposes a grey wolf algorithm based on a dynamic spiral hunting mechanism. This enhancement endows the algorithm with improved convergence speed and higher robustness, enabling more effective parameter tuning for the second-order fractional-order sliding mode controller (FSMC). The proposed algorithm demonstrates superior convergence speed and solution accuracy performance through testing and comparison. Finally, simulation verification under two conditions of the bridge crane system validates the effectiveness of the proposed approach. This paper proposes a grey wolf algorithm with a dynamic spiral hunting mechanism to enhance parameter tuning for the second-order fractional-order sliding mode controller (FSMC) in bridge crane systems. It addresses challenges in nonlinear dynamic systems, improving convergence speed and robustness. Testing shows superior performance in convergence speed and solution accuracy. Simulation verification confirms the effectiveness of the proposed approach in anti-swing control of bridge crane payloads, crucial for managing spatial swing under uncertainties. image

关键词： grey wolf algorithm overhead crane parameter optimization type-2 fuzzy sliding mode controller

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Virtual Inertia Optimization Allocation Based On grey wolf algorithm

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第11期27卷 3453-3462页

作者： Wu, Genzhu Nan, Dongliang Duan, Yu Zhang, Lu Zhu, Ziming Chang, Xiqiang Xinjiang Univ Sch Elect Engn Urumqi 830046 Xinjiang Peoples R China State Grid Xinjiang Elect Power Co Ltd Elect Power Sci Res Inst Urumqi 831000 Xinjiang Peoples R China

To address the optimal allocation of virtual inertia as a replacement for rotating inertia in power systems, this paper proposes a virtual inertia optimal allocation method. First, the calculation method for determining the minimum inertia requirement of power systems with high penetration of renewable energy is clarified. Next, considering factors like frequency stability and virtual inertia investment costs, a virtual inertia optimization allocation model is constructed with the goals of minimizing the frequency security index and investment costs, subject to constraints such as critical inertia, rate of change of frequency, and maximum frequency deviation. And the grey wolf algorithm is utilized to solve this model. Finally, the modified WSCC 9 -bus system and IEEE 39 -bus system are simulated to validate the effectiveness and universality of the proposed model in optimally allocating virtual inertia while balancing frequency stability and investment costs.

关键词： virtual inertia critical inertia frequency stability investment cost grey wolf algorithm

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Mobile service selection in edge and cloud computing environment with grey wolf algorithm

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INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES 2022年第3期18卷 229-249页

作者： Zhu, Ming Meng, Siyuan Li, Jing Yan, Song Shandong Univ Technol Coll Comp Sci & Technol Zibo Peoples R China East China Nromal Univ Coll Comp Sci & Technol Shanghai Peoples R China East China Normal Univ Coll Data Sci & Engn Shanghai Peoples R China

The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented;specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.

关键词： mobile edge computing cloud computing grey wolf algorithm quality of services QoSs service selection

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Optimal power flow with FACTS devices using a Novel grey wolf algorithm 3

Optimal power flow with FACTS devices using a Novel Grey Wol...

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3rd IEEE International Conference on Science Technology Engineering & Management (ICONSTEM)

作者： Singh, Madhu Dutta, Susanta Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur India Kalyani Govt Engn Coll Dept Elect Engn Nadia India

ISBN: (纸本)9781509048557

In this paper a new novel meta-heuristic approach grey wolf algorithm is employed for solving OPF problems mainly focusing on Fuel cost minimization (with and without valve point effect) and transmission loss minimization with the incorporation of FACTS devices TCSC and TCPS. GWO approach is independent of gradient of the objective function for searching global optimum solution, focusing on behaviour of wolves in nature their hunting mechanism and leadership hierarchy, the proposed algorithm is implementedand tested successfully on IEEE 30 bus test power system. the simulation result indicates comparatively superior performance of GWO in terms of speed and global optimum solution in comparison to newly developed methods mentioned in literature for OPF.

关键词： grey wolf algorithm Static Flexible AC transmission system Optimal Power flow Thyristor controlled series capacitor(TCSC) and Thyristor controlled phase shifter(TCPS)

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Research on Frequency Support Technology of Offshore Wind Farm Based on Improved grey wolf algorithm

Research on Frequency Support Technology of Offshore Wind Fa...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Cai Hui Sun Wentao Peng Zhuyi Xu Sixuan Wang Quanquan Qi Wanchun State Grid Jiangsu Elect Power Co Ltd Econ & Tech Res Inst Nanjing Peoples R China

ISBN: (纸本)9798350339345

The power grid may experience a decrease in frequency response capabilities and encounter issues such as secondary frequency drops and wind turbines over-responding when large-scale offshore wind farms are integrated. This can have a serious impact on the stability of high-proportion renewable energy power systems. To address this problem, a coordinated control strategy that employs an improved droop control, power-based virtual inertia control, and the grey wolf algorithm is proposed in this paper. The strategy optimizes droop control tuning coefficients for various wind speed settings, improves control accuracy, enhances droop characteristics of wind turbines, reduces frequency overshoot during frequency regulation, and effectively tackles the issue of slow response that leads to frequency drop in the initial stages of frequency disturbance when traditional droop control is employed. This strategy serves as technical support for the development of a new power system that relies heavily on renewable energy.

关键词： large-scale offshore wind farms coordinated control strategy improved droop control power-based virtual inertia control grey wolf algorithm

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Enhanced grey wolf algorithm for Energy Efficient Wireless Sensor Networks

Enhanced Grey Wolf Algorithm for Energy Efficient Wireless S...

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Zooming Innovation in Consumer Technologies Conference (ZINC)

作者： Zivkovic, Miodrag Bacanin, Nebojsa Zivkovic, Tamara Strumberger, Ivana Tuba, Eva Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Univ Belgrade Sch Elect Engn Belgrade Serbia

ISBN: (纸本)9781728182599

Wireless sensor networks have entered a period of a rapid development, due to several novel technologies which have emerged in the past few years, such as Internet of Things and cloud computing. Miniature sensor nodes are integral components of numerous complex systems. The biggest problem for any wireless sensor network, in any possible application domain, is to maximize the overall network lifetime by improving the total energy consumption of the network. A large number of clustering algorithms have been implemented in the past decade, with a main goal to balance the energy consumption of each node in the network and to increase energy efficiency - the term known in literature as load balancing. One important representative of these traditional algorithms for load balancing which is still in frequent use is LEACH. On the other hand, swarm intelligence meaheuristics have recently been successfully applied in solving a large number of NP hard problems from the wireless sensor networks domain. In this paper, we propose an improved version of grey wolf algorithm, that has been applied to improve the network lifetime optimization. grey wolf algorithm was employed in forming the clusters and the cluster head selection process. As a part of our research, we have evaluated the performance of the proposed exploration enhanced grey wolf algorithm by comparing it to the traditional LEACH algorithm, basic grey wolf approach and particle swarm optimization, that were all tested under the same experimental conditions. Obtained results from conducted simulations have proven that our proposed metaheuristics performs better that other considered algorithms.

关键词： wireless sensor networks lifetime optimization metaheuristics optimization grey wolf algorithm LEACH

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grey wolf algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

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INFORMATION 2019年第12期10卷 398-398页

作者： Xuan, Hejun Lin, Lidan Qiao, Lanlan Zhou, Yang Xinyang Normal Univ Sch Comp Sci & Technol Xinyang 464000 Peoples R China Luo He Vocat Technol Coll Dept Infomat Engn Luohe 462000 Henan Peoples R China

Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

关键词： manycast RSA EONs multi-objective optimization grey wolf algorithm

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Joint optimization method of cell formation and layout problems with square constraints based on improved multi-objective grey wolf algorithm

Joint optimization method of cell formation and layout probl...

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2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024

作者： Li, Rui Shi, Qian School of Mechanical Engineering Tongji University Shanghai China

ISBN: (纸本)9798350386097

To adapt to the real-life production environment, cell formation problems (CFPs) and cell layout problems (CLPs) with square constraints are simultaneously optimized. Firstly, CFPs and CLPs are formally described. To increase the accuracy of the inter- and intra-cell layout design, the coordinates are adopted and the material handling cost is calculated in terms of the actual position of machines within the cells and regarding the dimensions of the machines and aisle distances. To deal with the proposed problem, an improved multi-objective grey wolf algorithm with non-dominated sorting is proposed. Simulation experiments indicate that the proposed improved algorithm is feasible and effective. © 2024 IEEE.

关键词： Cell formation grey wolf algorithm layout problem coordinates

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Maximum Power Point Tracking of Photovoltaic Array Under Partial Shaded Conditions Based on Improved grey wolf algorithm 3

Maximum Power Point Tracking of Photovoltaic Array Under Par...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Wang, Yu Chang, Xiaoheng College of Control Science and Engineering Bohai University Jinzhou China

ISBN: (纸本)9798350318180

The power and voltage curves of photovoltaic array in local shadow display a state with multiple peaks, the traditional maximum power point tracking control method is no longer suitable, so the improved grey wolf optimization algorithm is proposed. In this method, a nonlinear convergence factor equation is proposed, and dimension learning-based hunting strategy is adopted to improve the convergence speed and search diversity of the algorithm, which helps the algorithm to track the maximum power point faster and more accurately. Simulation results show that the improved algorithm has higher convergence speed, tracking efficiency and success rate. © 2023 IEEE.

关键词： dimension learning-based hunting grey wolf algorithm maximum power point tracking

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