1 |
X. J. Cai, S. J. Geng, J. B. Zhang, D. Wu, Z. H. Cui, W. S. Zhang, and J. J. Chen, "A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things," IEEE Trans. Ind. Inf., vol. 17, no. 11, pp. 7650-7658, Jan. 2021.
DOI
|
2 |
L. Z. Wang, G. von Laszewski, A. Younge, X. He, M. Kunze, J. Tao, and C. Fu, "Cloud Computing: a Perspective Study," New Generation Computing, vol. 28, no. 2, pp. 137-146, Jun. 2010.
DOI
|
3 |
X. Z. Kong, C. Lin, Y. X. Jiang, W. Yan, and X. W. Chu, "Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction," Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1068-1077, Jul. 2011.
DOI
|
4 |
Z. Y. Gao, Y. Wang, Y. F. Gao, and X. T. Ren, "Multiobjective noncooperative game model for cost-based task scheduling in cloud computing," Concurrency and Computation-Practice & Experience, vol. 32, no. 7, Dec. 2020.
|
5 |
M. Abd Elaziz, S. W. Xiong, K. P. N. Jayasena, and L. Li, "Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution," Knowledge-Based Syst., vol. 169, pp. 39-52, Apr. 2019.
DOI
|
6 |
A. Marahatta, S. Pirbhulal, F. Zhang, R. M. Parizi, K. K. R. Choo, and Z. Y. Liu, "Classification-Based and Energy-Efficient Dynamic Task Scheduling Scheme for Virtualized Cloud Data Center," IEEE Trans. Cloud Comput., vol. 9, no. 4, pp. 1376-1390, Oct. 2021.
DOI
|
7 |
H. Mahmoud, M. Thabet, M. H. Khafagy, and F. A. Omara, "Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm," IEEE Access, vol. 10, pp. 36140-36151, Mar. 2022.
DOI
|
8 |
I. Attiya, M. Abd Elaziz, L. Abualigah, T. N. Nguyen, and A. A. Abd El-Latif, "An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud," IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6264-6272, Sep. 2022.
DOI
|
9 |
Z. Tong, F. Ye, B. L. Liu, J. H. Cai, and J. Mei, "DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment," Neurocomputing, vol. 455, pp. 419-430, Sep. 2021.
DOI
|
10 |
T. Bezdan, M. Zivkovic, N. Bacanin, I. Strumberger, E. Tuba, and M. Tuba, "Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm," J. Intell. Fuzzy Syst., vol. 42, no. 1, pp. 411-423, Dec. 2021.
DOI
|
11 |
X. Cai, Y. Lan, Z. Zhang, J. Wen, Z. Cui, and W. S. Zhang, "A Many-objective Optimization based Federal Deep Generation Model for Enhancing Data Processing Capability in IOT," IEEE Trans. Ind. Inf., vol. 19, no, 1, pp. 561-569, 2023.
DOI
|
12 |
A. Younes, M. K. Elnahary, M. H. Alkinani, and H. H. El-Sayed, "Task Scheduling Optimization in Cloud Computing by Rao Algorithm," CMC-Comput. Mater. Continua, vol. 72, no. 3, pp. 4339-4356, Apr. 2022.
DOI
|
13 |
X. Zhu, L. T. Yang, H. Chen, J. Wang, S. Yin, and X. Liu, "Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds," IEEE Trans. Cloud Comput., vol. 2, no. 2, pp. 168-180, Apr. 2014.
DOI
|
14 |
Z. H. Cui, J. J. Zhang, D. Wu, X. J. Cai, H. Wang, W. S. Zhang, and J. J. Chen, "Hybrid many-objective particle swarm optimization algorithm for green coal production problem," Inf. Sci., vol. 518, pp. 256-271, May. 2020.
DOI
|
15 |
E. H. Houssein, A. G. Gad, Y. M. Wazery, and P. N. Suganthan, "Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends," Swarm Evol. Comput., vol. 62, pp. 100841, Apr. 2021.
DOI
|
16 |
Z. P. Peng, D. L. Cui, J. L. Zuo, Q. R. Li, B. Xu, and W. W. Lin, "Random task scheduling scheme based on reinforcement learning in cloud computing," Cluster Computing-the Journal of Networks Software Tools and Applications, vol. 18, no. 4, pp. 1595-1607, Sep. 2015.
|
17 |
Y. H. Xiong, S. Z. Huang, M. Wu, J. H. She, and K. Y. Jiang, "A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing," IEEE Trans. Cloud Comput., vol. 7, no. 3, pp. 597-610, Jul.-Sep. 2019.
DOI
|
18 |
M. Hussain, L. F. Wei, A. Lakhan, S. Wali, S. Ali, and A. Hussain, "Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing," Sustainable Computing-Informatics & Systems, vol. 30, Jun. 2021.
|
19 |
H. T. Yuan, H. Li, J. Bi, and M. C. Zhou, "Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers," IEEE Trans. Autom. Sci. Eng., vol. 18, no. 2, pp. 817-830, Feb. 2021.
DOI
|
20 |
B. Hu, Z. C. Cao, and M. C. Zhou, "Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption," IEEE Trans. Cloud Comput., vol. 10, no. 1, pp. 662-674, Jan. 2022.
DOI
|
21 |
J. H. Xiao, W. Y. Zhang, S. Zhang, and X. Y. Zhuang, "Game theory-based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm," Concurrent Engineering-Research and Applications, vol. 27, no. 4, pp. 314-330, Oct. 2019.
DOI
|
22 |
B. M. H. Zade, N. Mansouri, and M. M. Javidi, "SAEA: A security-aware and energy-aware task scheduling strategy by Parallel Squirrel Search Algorithm in cloud environment," Expert Syst. Appl., vol. 176, Aug. 2021.
|
23 |
S. E. Shukri, R. Al-Sayyed, A. Hudaib, and S. Mirjalili, "Enhanced multi-verse optimizer for task scheduling in cloud computing environments," Expert Syst. Appl., vol. 168, Apr. 2021.
|
24 |
R. Trestian, O. Ormond, and G. M. Muntean, "Game Theory-Based Network Selection: Solutions and Challenges," IEEE Commun. Surv. Tutorials, vol. 14, no. 4, pp. 1212-1231, Feb. 2012.
DOI
|
25 |
J. Zou, Q. Y. Li, S. X. Yang, J. H. Zheng, Z. Peng, and T. R. Pei, "A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model," Swarm Evol. Comput., vol. 44, pp. 247-259, Feb. 2019.
DOI
|
26 |
J. L. Xu, Z. X. Zhang, Z. M. Hu, L. Du, and X. J. Cai, "A many-objective optimized task allocation scheduling model in cloud computing," Applied Intelligence, vol. 51, no. 6, pp. 3293-3310, Nov. 2021.
DOI
|
27 |
X. J. Cai, S. J. Geng, D. Wu, J. H. Cai, and J. J. Chen, "A Multicloud-Model-Based Many-Objective Intelligent Algorithm for Efficient Task Scheduling in the Internet of Things," IEEE Internet Things J., vol. 8, no. 12, pp. 9645-9653, Jun. 2021.
DOI
|
28 |
B. Mc Ginley, J. Maher, C. O'Riordan, and F. Morgan, "Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection," IEEE Trans. Evol. Comput., vol. 15, no. 5, pp. 692-714, Oct. 2011.
DOI
|
29 |
M. G. Fiestras-Janeiro, I. Garcia-Jurado, A. Meca, and M. A. Mosquera, "Cooperative game theory and inventory management," Eur. J. Oper. Res., vol. 210, no. 3, pp. 459-466, May. 2011.
DOI
|
30 |
H. Emami, "Cloud task scheduling using enhanced sunflower optimization algorithm," Ict Express, vol. 8, no. 1, pp. 97-100, Mar. 2022.
DOI
|
31 |
X. J. Cai, Z. M. Hu, and J. J. Chen, "A many-objective optimization recommendation algorithm based on knowledge mining," Inf. Sci., vol. 537, pp. 148-161, Oct. 2020.
DOI
|
32 |
K. Deb, and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints," IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577-601, Aug. 2014.
DOI
|
33 |
Y. Tian, R. Cheng, X. Y. Zhang, and Y. C. Jin, "PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization," IEEE Computational Intelligence Magazine, vol. 12, no. 4, pp. 73-87, Nov. 2017.
DOI
|
34 |
P. A. N. Bosman, and D. Thierens, "The balance between proximity and diversity in multiobjective evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 7, no. 2, pp. 174-188, Apr. 2003.
DOI
|
35 |
H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, "Reference Point Specification in Inverted Generational Distance for Triangular Linear Pareto Front," IEEE Trans. Evol. Comput., vol. 22, no. 6, pp. 961-975, Dec. 2018.
DOI
|
36 |
R. Cheng, Y. C. Jin, M. Olhofer, and B. Sendhoff, "A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization," IEEE Trans. Evol. Comput., vol. 20, no. 5, pp. 773-791, Oct. 2016.
DOI
|
37 |
S. X. Yang, M. Q. Li, X. H. Liu, and J. H. Zheng, "A Grid-Based Evolutionary Algorithm for Many-Objective Optimization," IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 721-736, Oct. 2013.
DOI
|
38 |
X. J. Cai, S. J. Geng, D. Wu, and J. J. Chen, "Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction," Swarm Evol. Comput., vol. 63, Jun. 2021.
|
39 |
M. S. A. Khan, and R. Santhosh, "Task scheduling in cloud computing using hybrid optimization algorithm," Soft Comput., vol. 26, pp. 13069-13079, 2022.
DOI
|
40 |
P. Y. Zhang, and M. C. Zhou, "Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy," IEEE Trans. Autom. Sci. Eng., vol. 15, no. 2, pp. 772-783, Apr. 2018.
DOI
|