Computer Networks, Год журнала: 2024, Номер unknown, С. 110869 - 110869
Опубликована: Окт. 1, 2024
Язык: Английский
Computer Networks, Год журнала: 2024, Номер unknown, С. 110869 - 110869
Опубликована: Окт. 1, 2024
Язык: Английский
Computer Networks, Год журнала: 2025, Номер unknown, С. 111044 - 111044
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1Computer Networks, Год журнала: 2024, Номер 253, С. 110731 - 110731
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
6Опубликована: Янв. 1, 2025
Large-scale optimization constitutes a pivotal characteristic of numerous real-world problems, where large-scale evolutionary algorithms emerge as potent instrument for addressing such intricacies. However, existing methods are typically tailored to address only particular class problems and lack the versatility be readily adapted other or generalized across diverse problem domains. To issue above, this paper proposes window method, simple yet effective enhancement that can seamlessly integrated into low-dimensional bolster their performance in optimization. Specifically, method involves grouping subset randomly selected dimensions during each iteration, restricting population's evolution within window. Furthermore, effectiveness is analyzed, improved based on insights gained, including isometric segmentation individual-level length neural network-guided element. Extensive experiments single-objective, multi-objective, constrained discrete test with attributes demonstrate proposed significantly mitigates curse dimensionality enhances EAs settings.
Язык: Английский
Процитировано
0IEEE Transactions on Sustainable Computing, Год журнала: 2024, Номер 9(6), С. 948 - 957
Опубликована: Май 17, 2024
With the development of technology, unmanned aerial vehicles (UAVs) and Internet Things devices are widely used in smart agriculture, resulting significant energy consumption. In this paper, optimization problem for UAV-assisted mobile computing agriculture is modeled as a constrained multi-objective problem. By jointly optimizing deployment position UAVs, offloading location tasks, transmit power devices, resource allocation two objectives (total delay consumption) minimized simultaneously. view complex constraints, multiobjective algorithm named JO-DPTS proposed. The adopts dual-population two-stage approach to improve population convergence diversity. simulation results substantiate that exhibits superior performance compared other three state-of-the-art evolutionary algorithms.
Язык: Английский
Процитировано
1Applied Intelligence, Год журнала: 2024, Номер 54(21), С. 10780 - 10801
Опубликована: Авг. 27, 2024
Язык: Английский
Процитировано
0Computer Networks, Год журнала: 2024, Номер unknown, С. 110869 - 110869
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
0