*Result*: QLSA-MOEAD integration for precision task scheduling in heterogeneous computing environments.
Title:
QLSA-MOEAD integration for precision task scheduling in heterogeneous computing environments.
Authors:
Saad A; Faculty of AI, Machine Intelligence Dept, Minufiya University, Shebeen El-Kom, Egypt. abla.saad@ci.menofia.edu.eg., Abd El-Raouf O; Faculty of AI, Machine Intelligence Dept, Minufiya University, Shebeen El-Kom, Egypt., Hadhoud M; Faculty of Computers and Information, Information Technology Dept, Minufiya University, Shebeen El-Kom, Egypt., Kafafy A; Faculty of AI, Data Science Dept, Minufiya University, Shebeen El-Kom, Egypt.
Source:
Scientific reports [Sci Rep] 2026 Feb 17; Vol. 16 (1), pp. 7194. Date of Electronic Publication: 2026 Feb 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Samayoa, W. F., Crespo, M. L., Cicuttin, A. & Carrato, S. A survey on fpga-based heterogeneous clusters architectures. IEEE Access 11, 67679–67706 (2023). (PMID: 10.1109/ACCESS.2023.3288431)
Deng, W.et al. A novel multi-objective optimized dag task scheduling strategy for fog computing based on container migration mechanism. Wirel. Netw. 31, 1–15 (2024).
Gao, Y., Yi, H., Chen, H., Fang, X. & Zhao, S. A structure-aware dag scheduling and allocation on heterogeneous multicore systems. In: 2024 IEEE 14th International Symposium on Industrial Embedded Systems (SIES), pp. 26–33 (IEEE, 2024).
Song, Y., Li, C., Tian, L. & Song, H. A reinforcement learning based job scheduling algorithm for heterogeneous computing environment. Comput. Electr. Eng. 107, 108653 (2023). (PMID: 10.1016/j.compeleceng.2023.108653)
Sulaiman, M., Halim, Z., Lebbah, M., Waqas, M. & Tu, S. An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment. J. Grid Comput. 19, 1–31 (2021). (PMID: 10.1007/s10723-021-09552-4)
Mahfoudhi, R., Achour, S. & Mahjoub, Z. Parallel triangular matrix system solving on cpu-gpu system. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–6 (IEEE, 2016).
Mittal, S. & Vetter, J. S. A survey of cpu-gpu heterogeneous computing techniques. ACM Comput. Surv. (CSUR) 47(4), 1–35 (2015). (PMID: 10.1145/2788396)
Tiwari, M. & Vadhiyar, S. Cluster optimization algorithm based on cpu and gpu hybrid architecture for high-performance computing. J. Supercomput. 77(5), 1234–1250. https://doi.org/10.1007/s11227-021-03756-0 (2021). (PMID: 10.1007/s11227-021-03756-0)
Hao, Y., Zhao, C., Li, Z., Si, B. & Unger, H. A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization. Knowl.-Based Syst. 286, 111366 (2024). (PMID: 10.1016/j.knosys.2024.111366)
Topcuoglu, H., Hariri, S. & Wu, M.-Y. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002). (PMID: 10.1109/71.993206)
Tsai, C.-H. et al. A biogeography-based optimization with a greedy randomized adaptive search procedure and the 2-opt algorithm for the traveling salesman problem. Sustainability 15(6), 5111 (2023). (PMID: 10.3390/su15065111)
Almeida, L. S., Goerlandt, F., Pelot, R. & Sörensen, K. A greedy randomized adaptive search procedure (grasp) for the multi-vehicle prize collecting arc routing for connectivity problem. Comput. Oper. Res. 143, 105804 (2022). (PMID: 10.1016/j.cor.2022.105804)
Guilmeau, T., Chouzenoux, E. & Elvira, V. Simulated annealing: A review and a new scheme. In: 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 101–105 (IEEE, 2021).
Glover, F. Tabu search: A tutorial. Interfaces 20(4), 74–94 (1990). (PMID: 10.1287/inte.20.4.74)
Tlili, T., Ben Nasser, S., Chicano, F. & Krichen, S. Tabu search-based hyper-heuristic for solving the heterogeneous ambulance routing problem with time windows. Int. J. Intell. Transp. Syst. Res. 22(2), 446–461 (2024).
Feng, X., Zhao, F., Jiang, G., Tao, T. & Mei, X. A tabu memory based iterated greedy algorithm for the distributed heterogeneous permutation flowshop scheduling problem with the total tardiness criterion. Expert Syst. Appl. 238, 121790 (2024). (PMID: 10.1016/j.eswa.2023.121790)
Abla Saada, O.A., Hadhoudb, M. & Kafafy, A. Comparative study of intelligent scheduling algorithms for heterogeneous systems. Environments 1, 2 (2024).
Hosseini Shirvani, M. & Noorian Talouki, R. Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach. Complex Intell. Syst. 8(2), 1085–1114 (2022). (PMID: 10.1007/s40747-021-00528-1)
Akbari, M. Hybrid approach based on cuckoo optimization algorithm and genetic algorithm for task scheduling. Evol. Intell. 14(4), 1931–1947 (2021). (PMID: 10.1007/s12065-020-00471-z)
Behera, I. & Sobhanayak, S. Task scheduling optimization in heterogeneous cloud computing environments: A hybrid ga-gwo approach. J. Parallel Distrib. Comput. 183, 104766 https://doi.org/10.1016/j.jpdc.2023.104766 (2024).
Imene, L., Sihem, S., Okba, K. & Mohamed, B. A third generation genetic algorithm nsgaiii for task scheduling in cloud computing. J. King Saud Univ. - Comput. Inf. Sci. 34(9), 7515–7529. https://doi.org/10.1016/j.jksuci.2022.03.017 (2022). (PMID: 10.1016/j.jksuci.2022.03.017)
Hosseini Shirvani, M. A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges. J. Supercomput. 80(7), 9384–9437 (2024). (PMID: 10.1007/s11227-023-05806-y)
Saad, A., Kafafy, A., Abd El Raouf, O. & El-Hefnawy, N. A grasp-simulated annealing approach applied to solve multi-processor task scheduling problems. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), pp. 310–315 (IEEE, 2019).
Movahedi, Z., Defude, B. & Hosseininia, A. M. An efficient population-based multi-objective task scheduling approach in fog computing systems. J. Cloud Comput. 10(1), 53 (2021). (PMID: 10.1186/s13677-021-00264-4)
Shukla, P. & Pandey, S. Motors: multi-objective task offloading and resource scheduling algorithm for heterogeneous fog-cloud computing scenario. J. Supercomput. 80(15), 22315–22361 (2024). (PMID: 10.1007/s11227-024-06315-2)
Saad, A., Abdel-Raouf, O., Hadhoud, M. & Kafafy, A. Enhanced multi-objective scheduling for heterogeneous computing platforms using hybrid moead with sa and ts-guided initialization strategies. J. Supercomput. 81(7), 853 (2025). (PMID: 10.1007/s11227-025-07278-8)
Wang, S. & Zhou, A. Regularity evolution for multiobjective optimization. IEEE Trans. Evol. Comput. 28, 1470–1483 (2023).
Xu, Y., Li, K., Hu, J. & Li, K. A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014). (PMID: 10.1016/j.ins.2014.02.122)
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn. (MIT Press, Cambridge, MA, USA, 2018).
Zhang, Q. & Li, H. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007). (PMID: 10.1109/TEVC.2007.892759)
Wang, Z., He, M., Wu, J., Chen, H. & Cao, Y. An improved moea/d for low-carbon many-objective flexible job shop scheduling problem. Comput. Ind. Eng. 188, 109926 (2024). (PMID: 10.1016/j.cie.2024.109926)
Li, R., Xu, H., Gu, Y. & Yang, J. Memory-based adaptive moead algorithm for multi-objective green fuzzy flexible job shop scheduling problem. In: Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, pp. 470–477 (2024).
Li, H. & Zhang, Q. Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans. Evol. Comput. 13(2), 284–302 (2008). (PMID: 10.1109/TEVC.2008.925798)
Saad, A., Kafafy, A., Abd-El-Raof, O. & El-Hefnawy, N. A grasp-genetic metaheuristic applied on multi-processor task scheduling systems. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 109–115 https://doi.org/10.1109/ICCES.2018.8639377 (2018).
Coello, C. A. C., Van Veldhuizen, D. A. & Lamont, G. B. Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, New York, NY, 2007).
Guerreiro, A. P., Fonseca, C. M. & Paquete, L. The hypervolume indicator: Computational problems and algorithms. ACM Comput. Surv. (CSUR) 54(6), 1–42 (2021). (PMID: 10.1145/3453474)
Shirvani, M. H. A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous -distributed computing systems. Eng. Appl. Artif. Intell. 90, 103501 (2020). (PMID: 10.1016/j.engappai.2020.103501)
Singh, J. & Singh, G. Improved task scheduling on parallel system using genetic algorithm. Int. J. Comput. Appl. 39(17), 17–22 (2012).
Deelman, E. et al. Pegasus: A framework for mapping complex scientific workflows onto distributed systems. Scientific Programming 13(3), 128026. https://doi.org/10.1155/2005/128026 (2005). (PMID: 10.1155/2005/128026)
Bharathi, S. et al. Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-Scale Science, pp. 1–10 https://doi.org/10.1109/WORKS.2008.4723958 (2008).
Intel Corporation: Intel®Xeon®Processor E5-1600/E5-2600 V3 Product Families Datasheet, Volume 1. Intel Corporation, https://www.intel.com/content/dam/www/public/us/en/documents/datasheets/xeon-e5-v3-datasheet-vol-1.pdf (2014).
NVIDIA Corporation: NVIDIA Tesla V100 GPU Accelerator. Datasheet. https://images.nvidia.com/content/technologies/volta/pdf/tesla-volta-v100-datasheet-letter-fnl-web.pdf.
Xilinx Inc.: Kintex UltraScale+ FPGAs Data Sheet: DC and AC Switching Characteristics. AMD Xilinx, AMD Xilinx. DS922. https://docs.xilinx.com/v/u/en-US/ds922-kintex-ultrascale-plus (2019).
Asghari Alaie, Y., Hosseini Shirvani, M. & Rahmani, A. M. A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach. J. Supercomput. 79(2), 1451–1503 (2023). (PMID: 10.1007/s11227-022-04703-0)
Chen, W., Qiao, X., Wei, J. & Huang, T. Deep reinforcement learning based resource allocation for cloud workflow systems. Inf. Sci. 551, 190–211 (2021).
Fang, J., Zhang, J., Lu, S., Zhao, H.: Exploration on task scheduling strategy for cpu-gpu heterogeneous computing system. In: 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 306–311 (IEEE, 2020).
Aldinucci, M. et al. The deephealth toolkit: A key european free and open-source software for deep learning and computer vision ready to exploit heterogeneous hpc and cloud architectures. In: Technologies and Applications for Big Data Value, pp. 183–202. (Springer, 2022).
Samayoa, W. F., Crespo, M. L., Cicuttin, A. & Carrato, S. A survey on fpga-based heterogeneous clusters architectures. IEEE Access 11, 67679–67706 (2023). (PMID: 10.1109/ACCESS.2023.3288431)
Deng, W.et al. A novel multi-objective optimized dag task scheduling strategy for fog computing based on container migration mechanism. Wirel. Netw. 31, 1–15 (2024).
Gao, Y., Yi, H., Chen, H., Fang, X. & Zhao, S. A structure-aware dag scheduling and allocation on heterogeneous multicore systems. In: 2024 IEEE 14th International Symposium on Industrial Embedded Systems (SIES), pp. 26–33 (IEEE, 2024).
Song, Y., Li, C., Tian, L. & Song, H. A reinforcement learning based job scheduling algorithm for heterogeneous computing environment. Comput. Electr. Eng. 107, 108653 (2023). (PMID: 10.1016/j.compeleceng.2023.108653)
Sulaiman, M., Halim, Z., Lebbah, M., Waqas, M. & Tu, S. An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment. J. Grid Comput. 19, 1–31 (2021). (PMID: 10.1007/s10723-021-09552-4)
Mahfoudhi, R., Achour, S. & Mahjoub, Z. Parallel triangular matrix system solving on cpu-gpu system. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–6 (IEEE, 2016).
Mittal, S. & Vetter, J. S. A survey of cpu-gpu heterogeneous computing techniques. ACM Comput. Surv. (CSUR) 47(4), 1–35 (2015). (PMID: 10.1145/2788396)
Tiwari, M. & Vadhiyar, S. Cluster optimization algorithm based on cpu and gpu hybrid architecture for high-performance computing. J. Supercomput. 77(5), 1234–1250. https://doi.org/10.1007/s11227-021-03756-0 (2021). (PMID: 10.1007/s11227-021-03756-0)
Hao, Y., Zhao, C., Li, Z., Si, B. & Unger, H. A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization. Knowl.-Based Syst. 286, 111366 (2024). (PMID: 10.1016/j.knosys.2024.111366)
Topcuoglu, H., Hariri, S. & Wu, M.-Y. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002). (PMID: 10.1109/71.993206)
Tsai, C.-H. et al. A biogeography-based optimization with a greedy randomized adaptive search procedure and the 2-opt algorithm for the traveling salesman problem. Sustainability 15(6), 5111 (2023). (PMID: 10.3390/su15065111)
Almeida, L. S., Goerlandt, F., Pelot, R. & Sörensen, K. A greedy randomized adaptive search procedure (grasp) for the multi-vehicle prize collecting arc routing for connectivity problem. Comput. Oper. Res. 143, 105804 (2022). (PMID: 10.1016/j.cor.2022.105804)
Guilmeau, T., Chouzenoux, E. & Elvira, V. Simulated annealing: A review and a new scheme. In: 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 101–105 (IEEE, 2021).
Glover, F. Tabu search: A tutorial. Interfaces 20(4), 74–94 (1990). (PMID: 10.1287/inte.20.4.74)
Tlili, T., Ben Nasser, S., Chicano, F. & Krichen, S. Tabu search-based hyper-heuristic for solving the heterogeneous ambulance routing problem with time windows. Int. J. Intell. Transp. Syst. Res. 22(2), 446–461 (2024).
Feng, X., Zhao, F., Jiang, G., Tao, T. & Mei, X. A tabu memory based iterated greedy algorithm for the distributed heterogeneous permutation flowshop scheduling problem with the total tardiness criterion. Expert Syst. Appl. 238, 121790 (2024). (PMID: 10.1016/j.eswa.2023.121790)
Abla Saada, O.A., Hadhoudb, M. & Kafafy, A. Comparative study of intelligent scheduling algorithms for heterogeneous systems. Environments 1, 2 (2024).
Hosseini Shirvani, M. & Noorian Talouki, R. Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach. Complex Intell. Syst. 8(2), 1085–1114 (2022). (PMID: 10.1007/s40747-021-00528-1)
Akbari, M. Hybrid approach based on cuckoo optimization algorithm and genetic algorithm for task scheduling. Evol. Intell. 14(4), 1931–1947 (2021). (PMID: 10.1007/s12065-020-00471-z)
Behera, I. & Sobhanayak, S. Task scheduling optimization in heterogeneous cloud computing environments: A hybrid ga-gwo approach. J. Parallel Distrib. Comput. 183, 104766 https://doi.org/10.1016/j.jpdc.2023.104766 (2024).
Imene, L., Sihem, S., Okba, K. & Mohamed, B. A third generation genetic algorithm nsgaiii for task scheduling in cloud computing. J. King Saud Univ. - Comput. Inf. Sci. 34(9), 7515–7529. https://doi.org/10.1016/j.jksuci.2022.03.017 (2022). (PMID: 10.1016/j.jksuci.2022.03.017)
Hosseini Shirvani, M. A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges. J. Supercomput. 80(7), 9384–9437 (2024). (PMID: 10.1007/s11227-023-05806-y)
Saad, A., Kafafy, A., Abd El Raouf, O. & El-Hefnawy, N. A grasp-simulated annealing approach applied to solve multi-processor task scheduling problems. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), pp. 310–315 (IEEE, 2019).
Movahedi, Z., Defude, B. & Hosseininia, A. M. An efficient population-based multi-objective task scheduling approach in fog computing systems. J. Cloud Comput. 10(1), 53 (2021). (PMID: 10.1186/s13677-021-00264-4)
Shukla, P. & Pandey, S. Motors: multi-objective task offloading and resource scheduling algorithm for heterogeneous fog-cloud computing scenario. J. Supercomput. 80(15), 22315–22361 (2024). (PMID: 10.1007/s11227-024-06315-2)
Saad, A., Abdel-Raouf, O., Hadhoud, M. & Kafafy, A. Enhanced multi-objective scheduling for heterogeneous computing platforms using hybrid moead with sa and ts-guided initialization strategies. J. Supercomput. 81(7), 853 (2025). (PMID: 10.1007/s11227-025-07278-8)
Wang, S. & Zhou, A. Regularity evolution for multiobjective optimization. IEEE Trans. Evol. Comput. 28, 1470–1483 (2023).
Xu, Y., Li, K., Hu, J. & Li, K. A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014). (PMID: 10.1016/j.ins.2014.02.122)
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn. (MIT Press, Cambridge, MA, USA, 2018).
Zhang, Q. & Li, H. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007). (PMID: 10.1109/TEVC.2007.892759)
Wang, Z., He, M., Wu, J., Chen, H. & Cao, Y. An improved moea/d for low-carbon many-objective flexible job shop scheduling problem. Comput. Ind. Eng. 188, 109926 (2024). (PMID: 10.1016/j.cie.2024.109926)
Li, R., Xu, H., Gu, Y. & Yang, J. Memory-based adaptive moead algorithm for multi-objective green fuzzy flexible job shop scheduling problem. In: Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, pp. 470–477 (2024).
Li, H. & Zhang, Q. Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans. Evol. Comput. 13(2), 284–302 (2008). (PMID: 10.1109/TEVC.2008.925798)
Saad, A., Kafafy, A., Abd-El-Raof, O. & El-Hefnawy, N. A grasp-genetic metaheuristic applied on multi-processor task scheduling systems. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 109–115 https://doi.org/10.1109/ICCES.2018.8639377 (2018).
Coello, C. A. C., Van Veldhuizen, D. A. & Lamont, G. B. Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, New York, NY, 2007).
Guerreiro, A. P., Fonseca, C. M. & Paquete, L. The hypervolume indicator: Computational problems and algorithms. ACM Comput. Surv. (CSUR) 54(6), 1–42 (2021). (PMID: 10.1145/3453474)
Shirvani, M. H. A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous -distributed computing systems. Eng. Appl. Artif. Intell. 90, 103501 (2020). (PMID: 10.1016/j.engappai.2020.103501)
Singh, J. & Singh, G. Improved task scheduling on parallel system using genetic algorithm. Int. J. Comput. Appl. 39(17), 17–22 (2012).
Deelman, E. et al. Pegasus: A framework for mapping complex scientific workflows onto distributed systems. Scientific Programming 13(3), 128026. https://doi.org/10.1155/2005/128026 (2005). (PMID: 10.1155/2005/128026)
Bharathi, S. et al. Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-Scale Science, pp. 1–10 https://doi.org/10.1109/WORKS.2008.4723958 (2008).
Intel Corporation: Intel®Xeon®Processor E5-1600/E5-2600 V3 Product Families Datasheet, Volume 1. Intel Corporation, https://www.intel.com/content/dam/www/public/us/en/documents/datasheets/xeon-e5-v3-datasheet-vol-1.pdf (2014).
NVIDIA Corporation: NVIDIA Tesla V100 GPU Accelerator. Datasheet. https://images.nvidia.com/content/technologies/volta/pdf/tesla-volta-v100-datasheet-letter-fnl-web.pdf.
Xilinx Inc.: Kintex UltraScale+ FPGAs Data Sheet: DC and AC Switching Characteristics. AMD Xilinx, AMD Xilinx. DS922. https://docs.xilinx.com/v/u/en-US/ds922-kintex-ultrascale-plus (2019).
Asghari Alaie, Y., Hosseini Shirvani, M. & Rahmani, A. M. A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach. J. Supercomput. 79(2), 1451–1503 (2023). (PMID: 10.1007/s11227-022-04703-0)
Chen, W., Qiao, X., Wei, J. & Huang, T. Deep reinforcement learning based resource allocation for cloud workflow systems. Inf. Sci. 551, 190–211 (2021).
Contributed Indexing:
Keywords: DAG (Directed; Heterogeneous Computing Environment; MOEA/D; Q-Learning; Simulated Annealing
Entry Date(s):
Date Created: 20260217 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12920661
DOI:
10.1038/s41598-026-36916-1
PMID:
41702958
Database:
MEDLINE
*Further Information*
*Heterogeneous computing infrastructures integrating CPUs, GPUs, and FPGAs present critical challenges in efficient task scheduling due to hardware diversity, complex task dependencies, and conflicting optimization objectives. This work formulates workflow scheduling as a multi-objective optimization problem that minimizes makespan and maximizes resource utilization. For synthetic benchmarks (FFT, Molecular), the approach minimizes makespan and maximizes resource utilization. For the CyberShake seismic workflow, energy consumption is added as a third objective. This research proposes QLSA-MOEAD, a hybrid framework combining three complementary mechanisms: Q-learning for intelligent initialization, Simulated Annealing for local refinement, and MOEA/D for multi-objective decomposition. This integration balances exploration and exploitation effectively. Comprehensive evaluations on 20 test cases (structured FFT, unstructured molecular, and real-world CyberShake workflows) show superior performance. QLSA-MOEAD achieves the best solution quality in 14 out of 16 FFT/molecular cases and outperforms all baselines on CyberShake. A large-scale Montage workflow (100 tasks, 179 dependencies) validates scalability under real-time task arrivals. The framework maintains excellent convergence and diversity across different CCR levels. Q-learning achieves fast decision-making with 0.80-1.70 ms response time. Statistical validation (Wilcoxon and Friedman tests), ablation studies, and parameter sensitivity analysis confirm framework robustness. These results establish QLSA-MOEAD as an effective solution for both static and dynamic workflow scheduling in heterogeneous environments.
(© 2026. The Author(s).)*
*Declarations. Competing interests: The authors declare no competing interests.*