EDP Sciences logo
提交您的论文
搜索
Menu
首页 All issues Volume 15 (2024) Int. J. Simul. Multidisci. Des. Optim., 15 (2024) 3 参考文献
Open Access
期号
Int. J. Simul. Multidisci. Des. Optim.
卷号 15, 2024
文献编号 3
页数 12
DOI https://doi.org/10.1051/smdo/2024001
网上发表时间 2024年3月15日
  1. J. Békési, G. Dósa, G. Galambos, A first fit type algorithm for the coupled task scheduling problem with unit execution time and two exact delays, Eur. J. Oper. Res. 297, 844–852 (2022) [CrossRef] [Google Scholar]
  2. H. Nishikawa, K. Shimada, I. Taniguchi, H. Tomiyama, Mouldable fork-join task scheduling techniques with inter and intra-task communications, Int. J. Embed. Syst. 15, 69–81 (2022) [CrossRef] [Google Scholar]
  3. Y.W. Ti, S.K. Chen, W.C. Wang, A hierarchical particle swarm optimisation algorithm for cloud computing environment, Int. J. Inf. Comput. Secur. 18, 12–26 (2022) [Google Scholar]
  4. Y.J. Chiu, B. Li, S.R. Jian, S.Y. Lien, J.M. Yi, Improved particle swarm optimization algorithm for photovoltaic system under local shading, J. Chin. Inst. Eng. 45, 632–643 (2022) [CrossRef] [Google Scholar]
  5. W. Zhang, Y. Ran, G. Zhang, Y. Shao, Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm, Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. 236, 4565–4576 (2022) [CrossRef] [Google Scholar]
  6. I. Dagal, B. Akın, E. Akboy, Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems, Int. J. Energy Res. 46, 8742–8759 (2022) [CrossRef] [Google Scholar]
  7. M. Zhang, Prediction of rockburst hazard based on particle swarm algorithm and neural network, Neural Comput. Appl. 34, 2649–2659 (2022) [CrossRef] [Google Scholar]
  8. Y. Bi, A. Lam, H. Quan, C. Wang, A comprehensively improved particle swarm optimization algorithm to guarantee particle activity, Russ. Phys. J. 64, 866–875 (2021) [CrossRef] [Google Scholar]
  9. H. Liu, S. Duan, H. Luo, A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization, Mater. Test. 64, 1051–1089 (2022) [CrossRef] [Google Scholar]
  10. M.Q.H. Abadi, S. Rahmati, A. Sharifi, M. Ahmadi, HSSAGA: designation and scheduling of nurses for taking care of COVID-19 patients using novel method of hybrid salp swarm algorithm and genetic algorithm, Appl. Soft Comput. 108, 107449 (2021) [CrossRef] [Google Scholar]
  11. I. Dagal, B. Akın, E. Akboy, A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications, Ain Shams Eng. J. 13, 101747 (2022) [CrossRef] [Google Scholar]
  12. A. Chalh, R. Chaibi, A.E. Hammoumi, S. Motahhir, A.E. Ghzizal, M. Al-Dhaifallah, A novel MPPT design based on the seagull optimization algorithm for photovoltaic systems operating under partial shading, Sci. Rep. 12, 21804 (2022) [CrossRef] [Google Scholar]
  13. A.L.W. Ibrahim, F. Zhijian, H.M.H. Farh, I. Dagal, A.A. Al-Shamma'a, A.M. Al-Shaalan, Hybrid SSA-PSO based intelligent direct sliding-mode control for extracting maximum photovoltaic output power and regulating the DC-bus voltage, Int. J. Hydrog. Energy 51, 348–370 (2024) [CrossRef] [Google Scholar]
  14. Y. Guo, Z. Mustafaoglu, D. Koundal, Spam detection using bidirectional transformers and machine learning classifier algorithms, J. Comput. Cognit. Eng. 2, 5–9 (2022) [CrossRef] [Google Scholar]
  15. M.K. Marichelvam, M. Geetha, Solving industrial multiprocessor task scheduling problems using an improved monkey search algorithm, Int. J. Oper. Res. 41, 135–149 (2021) [CrossRef] [MathSciNet] [Google Scholar]
  16. J. Yu, M. Wang, Y. JH, S.M.S Arefzadeh, A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm, Circuit World 49, 294–304 (2023) [CrossRef] [Google Scholar]
  17. C.A. Rigo, L.O. Seman, E. Camponogara, E. Morsch Filho, E.A. Bezerra, P. Munari, A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service, Eur. J. Oper. Res. 303, 168–183 (2022) [CrossRef] [Google Scholar]
  18. J. Gao, X. Zhu, R. Zhang, Optimization of parallel test task scheduling with constraint satisfaction, J. Supercomput. 79, 7206–7227 (2023) [CrossRef] [Google Scholar]
  19. E. Xidias, V. Moulianitis, P. Azariadis, Optimal robot task scheduling based on adaptive neuro-fuzzy system and genetic algorithms, Int. J. Adv. Manuf. Technol. 115, 927–939 (2021) [CrossRef] [Google Scholar]
  20. Y. Natarajan, S. Kannan, G. Dhiman, Task scheduling in cloud using aco, Recent Adv. Comput. Sci. Commun. (Formerly: Recent Patents on Computer Science), 15, 348–353 (2022) [Google Scholar]
  21. T. Bezdan, M. Zivkovic, N. Bacanin, I. Strumberger, E. Tuba, M. Tuba, Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm, J. Intell. Fuzzy Syst. 42, 411–423 (2022) [Google Scholar]
  22. L. Wang, P. Zheng, Y. Ji, X. Chen, Multi-objective optimization of a Stirling cooler using particle swarm optimization algorithm, Sci. Technol. Built Environ. 28, 379–390 (2022) [CrossRef] [Google Scholar]
  23. J. Zhang, Y. Yang, An optimisation of 3D printing parameters of nanocomposites based on improved particle swarm optimisation algorithm, Int. J. Microstruct. Mater. Prop. 16, 266–277 (2023) [Google Scholar]
  24. Y.W. Chen, Dynamic interception point guidance algorithm based on particle swarm optimization, Meas. Control. 55, 983–995 (2022) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.