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 M...
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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 greywolfalgorithm, 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.
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 op...
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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.
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 ...
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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, greywolfoptimization (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.
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 ...
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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 b...
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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 sec...
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An improved greywolfoptimization (AGWO) algorithm based on arctangent inertia weight strategy is proposed to address the slow convergence speed and low optimization accuracy of the greywolfoptimization (GWO) algor...
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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 ki...
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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.
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 greywolfoptimization (QOGWO) algorithm is pre...
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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 greywolfoptimization (QOGWO) algorithm is presented to improve the greywolfoptimization (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.
Done in this manuscript, an analytical method, Variation Iteration Method (VIM) has been hybridized with an intelligent method the Gray wolfoptimization (GWO) algorithm, by solving nonlinear Ordinary Differential Equ...
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Done in this manuscript, an analytical method, Variation Iteration Method (VIM) has been hybridized with an intelligent method the Gray wolfoptimization (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.
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