Evolutionary Intelligence, Год журнала: 2024, Номер 18(1)
Опубликована: Ноя. 16, 2024
Язык: Английский
Evolutionary Intelligence, Год журнала: 2024, Номер 18(1)
Опубликована: Ноя. 16, 2024
Язык: Английский
Expert Systems with Applications, Год журнала: 2025, Номер 268, С. 126281 - 126281
Опубликована: Янв. 2, 2025
Язык: Английский
Процитировано
0Surface and Coatings Technology, Год журнала: 2025, Номер 497, С. 131765 - 131765
Опубликована: Янв. 10, 2025
Язык: Английский
Процитировано
0Alexandria Engineering Journal, Год журнала: 2025, Номер 117, С. 563 - 576
Опубликована: Янв. 20, 2025
Язык: Английский
Процитировано
0Applied Energy, Год журнала: 2025, Номер 390, С. 125807 - 125807
Опубликована: Апрель 2, 2025
Язык: Английский
Процитировано
0Journal of Computational Methods in Sciences and Engineering, Год журнала: 2025, Номер unknown
Опубликована: Апрель 24, 2025
In the context of increasing environmental challenges and demand for sustainable development, traditional resource scheduling models in business management often fail to balance economic efficiency with constraints. To address this gap, study proposes an enhanced Particle Swarm Optimization (PSO) algorithm, termed OBLPSO, which integrates Opposition-Based Learning (OBL) a perturbation mechanism. First, OBL generates high-quality initial population improve solution diversity, while cosine curve adaptive strategy dynamically adjusts inertia weights global exploration local exploitation. Additionally, mechanism expands search range, preventing premature convergence. A multi-objective optimization model is established, incorporating task time, cost, impact (e.g., energy consumption pollutant emissions) maximize utilization minimize ecological harm. Experimental results demonstrate that OBLPSO reduces processing time by 29.7% 16.1% compared benchmark algorithms ACO, GA, standard PSO) under large-scale tasks (2000 tasks). The proposed method provides robust enterprise environment
Язык: Английский
Процитировано
0International Journal of Management Science and Engineering Management, Год журнала: 2025, Номер unknown, С. 1 - 11
Опубликована: Апрель 30, 2025
Язык: Английский
Процитировано
0PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0316281 - e0316281
Опубликована: Дек. 30, 2024
Optimizing energy resources is a major priority these days. Increasing household demand often leads to the deterioration of poorly sized distribution networks. This paper presents method for compensation and optimization in radial systems (ORDS). By integrating distributed generations (DG), an approach used evaluate voltage power profiles, as well losses on (PLRDSs). After generations, improved profiles are established. A potential solution blackouts (PCB) can also be use hybrid generation (HDGSs) that reinforce networks (RDNs) by improving quality. Accordingly, proposed configuration system shown this work inject multiple renewable sources (MRES) from selected regulated nodes. The feasibility evaluated using particle swarm (PSO), which was locate stable nodes locations, sensitive fluctuations. based evaluation IEEE 33 bus 69 standards MATLAB-based establishes objective function converges more quickly optimal results.
Язык: Английский
Процитировано
2Evolutionary Intelligence, Год журнала: 2024, Номер 18(1)
Опубликована: Ноя. 16, 2024
Язык: Английский
Процитировано
0