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43rd Chinese Control Conference (CCC)

作者： Zhang, Mingyu Liu, Feng Wang, Yujie Yan, Yaqing Wei, Zheng Xian Beijing Jiaotong Univ Inst Adv Control Syst Beijing 100044 Peoples R China China State Shipbldg Corp Syst Engn Res Inst Beijing 100094 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

To address the challenges of multi-unmanned aerial vehicle (UAV) trajectory planning in three-dimensional complex environments. this study proposes a method based on the Improved grey wolf optimization algorithm for Multi-UAV 3D Trajectory Planning. The approach simulates real geographical environments, establishing three-dimensional terrain and no-fly zone models. Building upon the foundation of single UAV trajectory planning, the proposed method incorporates collaborative constraints for multi-UAV coordination, forming an evaluation function for multi-UAV collaborative trajectory planning. In order to solve the limitations of the standard grey wolf algorithm, which is prone to local optima and exhibits suboptimal convergence rates, an improved convergence factor strategy and a reward-penalty mechanism in the optimization process are introduced. Comparative evaluations against several relevant algorithms validate the superior feasibility of the proposed approach. Simulation results demonstrate that, compared to other algorithms. the proposed method achieves smaller trajectory costs, faster convergence rates, and more stable performance.

关键词： grey wolf optimization algorithm Multi-UAV 3D Trajectory Planning Convergence Factor Reward-Penalty Mechanism

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A grey wolf optimization algorithm for Multi Strategy Network Coverage Based on Roulette Wheel 24

A Grey Wolf Optimization Algorithm for Multi Strategy Networ...

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11th International Conference on Wireless Communication and Sensor Networks (icWCSN)

作者： Ma, Qianli Qi, Xin Qian, Huimeng Cui, Zhiheng Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9798400709005

The key to wireless sensor network coverage lies in how to achieve full coverage of the region with as few sensor nodes as possible. The research on wireless sensor network coverage often faces problems such as low optimization accuracy, slow convergence speed, and susceptibility to premature convergence. To address these issues, A multi strategy network coverage grey wolf optimization algorithm (MSGWO) based on roulette wheel is proposed. Firstly, a mixed mapping of Tent and Circle maps is used to map the initial distribution of wireless sensor nodes, so that the initial distribution of sensor nodes is as evenly distributed as possible in the space. Secondly, as the iteration cycle changes, multi strategy position updates are performed on the gray wolves that meet the conditions in the grey wolf optimization algorithm (GWO) to improve the ability to jump out of local convergence. Finally, in the iterative optimization of the grey wolf optimization algorithm, a leadership strategy based on the roulette wheel method was used to expand the global search range of the algorithm. Simulation experiments have shown that MSGWO has higher coverage and less time consumption.

关键词： wireless sensor network coverage optimization grey wolf optimization algorithm coverage rate

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Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 18-42页

作者： Sefati, SeyedSalar Mousavinasab, Maryamsadat Zareh Farkhady, Roya Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Inst Higher Educ Roshdiyeh Dept Comp Engn Tabriz Iran

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal.

关键词： Cloud computing Load balancing grey wolf optimization algorithm Reliability

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Optimal Operation Strategy for Integrated Energy Systems Based on Improved grey wolf optimization algorithm 3

Optimal Operation Strategy for Integrated Energy Systems Bas...

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3rd International Conference on Power Systems and Electrical Technology, PSET 2024

作者： Jiang, Chongying Qin, Shaoji Lin, Kaidong Liu, Siliang Deng, Fengqiang Zhang, Jun South China University of Technology School of Electrical Power Guangzhou China

ISBN: (纸本)9798350353204

The optimization of the multi-energy storage configuration within the Integrated Energy System (IES) is achieved through the utilization of an Improved grey wolf optimization algorithm (IGWO), which enhances economic efficiency, reliability, and environmental sustainability. An IES model incorporating wind and photovoltaic generation along with various storage devices has been established, alongside the development of a multi-objective model focused on minimizing costs and emissions. The introduction of a Gaussian mutation strategy within the improved algorithm bolsters exploratory capabilities and enhances population diversity. Furthermore, an entropy-weight method has been employed to automatically determine the weights of evaluation criteria, ensuring the objectivity and rigor of decision-making. Validation through case studies has confirmed the method's effectiveness in enhancing the cost-efficiency and environmental performance of the IES, showcasing its capability to manage energy flows effectively, reduce costs, and minimize environmental impacts while maintaining system performance. © 2024 IEEE.

关键词： Entropy Weight Method grey wolf optimization algorithm Integrated Energy System Multi-energy Storage Multi-objective optimization

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Optimal scheduling of microgrids based on multi-objective improved grey wolf optimization algorithm 4

Optimal scheduling of microgrids based on multi-objective im...

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4th International Conference on Electronics, Circuits and Information Engineering, ECIE 2024

作者： Hu, Bowei Xu, Minghu Ji, Jianwei Shenyang Agricultural University College of Information and Electrical Engineering Shenyang China State Grid Liaoning electric power supply skills training center Jinzhou China

ISBN: (纸本)9798350388312

The development of an optimal scheduling strategy is crucial for microgrid systems, which can ensure the cost-effectiveness of the system. For this reason, this paper proposes a microgrid optimal scheduling strategy based on the multi-objective improved grey wolf algorithm, to further improve the comprehensive benefits of microgrid optimal scheduling. First, on the traditional microgrid model containing multiple distributed power sources, a comprehensive optimal scheduling model is constructed by considering the environmental benefits and combining the demand-side load profile and time-sharing tariff. Then, the objective function for maximizing the comprehensive benefits is designed and solved by using the multi-objective grey wolf algorithm based on multi-objective co-optimization for optimal scheduling. Finally, the effectiveness of the method proposed in this paper is verified by simulation analysis and comparison of actual examples. © 2024 IEEE.

关键词： comprehensive benefits grey wolf optimization algorithm Microgrid optimal scheduling

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Improved grey wolf optimization algorithm Based on Secant Inertia Weight Strategy 3

Improved Grey Wolf Optimization Algorithm Based on Secant In...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Jiaming, Pi Beijing Sport University School of Journalism and Communication Beijing China

ISBN: (纸本)9798350341546

Given the low solution accuracy, slow convergence speed, and easy entrapment in local optimal solution of grey wolf optimization algorithm (GWO) in solving function optimization problems, this paper introduces the secant function and proposes an improved grey wolf optimization based on secant inertia weight strategy (SGWO). Compared with six other swarm intelligence optimization algorithms, the simulation experimental results on five standard test functions demonstrate that SGWO can effectively balance the global and local search ability, and significantly improve the convergence speed, accuracy, and stability. © 2023 IEEE.

关键词： Convergence speed grey wolf optimization algorithm Inertia weight Secant function Stability

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Improved grey wolf optimization algorithm Based on Arctangent Inertia Weight 8

Improved Grey Wolf Optimization Algorithm Based on Arctangen...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Shi, JunFang Yang, Yong Chen, Mingyao College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350331035

An improved grey wolf optimization (AGWO) algorithm based on arctangent inertia weight strategy is proposed to address the slow convergence speed and low optimization accuracy of the grey wolf optimization (GWO) algorithm. The AGWO algorithm has non-linear characteristics and can better balance the global and local search capabilities of the algorithm. Under special situation, AGWO can degenerate into GWO, AGWO is an extent of GWO. The search performance of AGWO is tested on five classic test functions. The experimental results show that AGWO has higher optimization performance than four classical swarm intelligence algorithms. © 2023 IEEE.

关键词： arctangent function grey wolf optimization algorithm inertia weight nonlinear characteristics optimization performance

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Dynamic grey wolf optimization algorithm Based on Quasi-Opposition Learning 1

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3rd International Conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep Learning (3DIT-MSP and DL)

作者： Wang, Tianlei Li, Junhui Liu, Renju Xu, Jinzhao Hao, Xiaoxi Kin, Kenneth Teo Tze Liang, Jiehong Wuyi Univ Fac Intelligent Mfg Jiangmen 529020 Guangdong Peoples R China Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Wuyi Univ Sch Math & Comp Sci Jiangmen 529020 Guangdong Peoples R China Univ Malaysia Sabah Fac Engn Kota Kinabalu Sabah Malaysia Jinming Machinery Mfg Co LTD Shantou Peoples R China

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

Although the structure of grey wolf optimization algorithm (GWO) is simple and easy to understand, it has the disadvantage of unbalanced exploration and exploitation. To solve the problem, this paper puts forward a kind of learning strategy based on quasi-opposition learning and dynamic search strategy improved grey wolf optimization algorithm. That is verified on the nine benchmark test functions, the experimental results proved that the improved grey wolf optimization algorithm has better search performance, compared with the cuckoo search algorithm (CS) and particle swarm optimization (PSO) algorithm at the same time, by contrast, improved algorithm has the advantages of larger.

关键词： Swarm intelligence grey wolf optimization algorithm Quasi-opposition-based learning

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Solving nonlinear differential equations using a hybrid method between the variational iteration method and grey wolf optimization algorithm

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2022年第2期25卷 573-585页

作者： Entesar, Ahmed Mahmood, Hiba Shuker Qasim, Omar Saber Univ Mosul Coll Comp Sci & Math Dept Math Mosul Iraq Univ Mosul Coll Educ Pure Sci Dept Math Mosul Iraq

Done in this manuscript, an analytical method, Variation Iteration Method (VIM) has been hybridized with an intelligent method the Gray wolf optimization (GWO) algorithm, by solving nonlinear Ordinary Differential Equations, where the results obtained from the analytical method have been improved by (GWO) algorithm. The results obtained by calculating the mean square error (MSE) and estimating the maximum absolute error (MAE) proved the efficiency of this method.

关键词： Variation iteration method grey wolf optimization algorithm Non-linear ordinary differentiation equations

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Antenna optimization Based on Quasi-Opposition grey wolf optimization algorithm 14

Antenna Optimization Based on Quasi-Opposition Grey Wolf Opt...

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14th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Zhu, Hao-Yun Li, Wei-Dong Zhao, Hai-Yan Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9781665467520

Performance optimization of an antenna is a non-linear, multi-dimensional and complex scheme, which can be solved by applying intelligence algorithms. A Quasi-Opposition grey wolf optimization (QOGWO) algorithm is presented to improve the grey wolf optimization (GWO) algorithm. It is prone to global optimality, high precision for complex problems and a fast convergence rate at a later stage. A wideband dual-polarized magneto-electric dipole antenna is simulated as an example. Compared to the GWO algorithm, the antenna optimized by the improved algorithm has a wider bandwidth, verifying the feasibility of the algorithm in the field of antenna design optimization.

关键词： Antenna Quasi-Opposition grey wolf optimization algorithm

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