How to cite this paper
Yeh, W., Liu, Z & Tseng, K. (2023). Bi-Objective simplified swarm optimization for fog computing task scheduling.International Journal of Industrial Engineering Computations , 14(4), 723-748.
Refrences
Binh, H. T. T., Anh, T. T., Son, D. B., Duc, P. A., & Nguyen, B. M. (2018, December). An evolutionary algorithm for solving task scheduling problem in cloud-fog computing environment. In Proceedings of the 9th International Symposium on Information and Communication Technology (pp. 397-404).
Bitam, S., Zeadally, S., & Mellouk, A. (2018). Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems, 12(4), 373-397.
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012, August). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13-16).
Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things journal, 3(6), 854-864.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
Czyzżak, P., & Jaszkiewicz, A. (1998). Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization. Journal of multi‐criteria decision analysis, 7(1), 34-47.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE internet of things journal, 3(6), 1171-1181.
Doğan, A., & Özgüner, F. (2005). Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems. The Computer Journal, 48(3), 300-314.
Fard, H. M., Prodan, R., Barrionuevo, J. J. D., & Fahringer, T. (2012). A multi-objective approach for workflow scheduling in heterogeneous environments. Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on,
Fieldsend, J. E., & Singh, S. (2002). A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence.
Han, K. K., Xie, Z. P., & Lv, X. (2018). Fog computing task scheduling strategy based on improved genetic algorithm. Computer Science, 4, 22.
He, J., Cheng, P., Shi, L., Chen, J., & Sun, Y. (2013). Time synchronization in WSNs: A maximum-value-based consensus approach. IEEE Transactions on Automatic Control, 59(3), 660-675.
Huang, C. L., & Yeh, W. C. (2019). A new SSO-based algorithm for the bi-objective time-constrained task scheduling problem in cloud computing services. arXiv preprint arXiv:1905.04855.
Jena, R. K. (2015). Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Computer Science, 57, 1219-1227.
Jiang, Y., Liu, Z., Chen, J.-H., Yeh, W.-C., & Huang, C.-L. (2023). A novel binary-addition simplified swarm optimization for generalized reliability redundancy allocation problem. Journal of Computational Design and Engineering, 10(2), 758-772.
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1‐2), 83-97.
Li, D., & Sun, X. (2006). Nonlinear integer programming (Vol. 84). Springer Science & Business Media.
Li, X. (2003). A non-dominated sorting particle swarm optimizer for multiobjective optimization. Genetic and Evolutionary Computation Conference.
Liu, J., Luo, X. G., Zhang, X. M., Zhang, F., & Li, B. N. (2013). Job scheduling model for cloud computing based on multi-objective genetic algorithm. International Journal of Computer Science Issues (IJCSI), 10(1), 134.
Matt, C. J. B., & Engineering, I. S. (2018). Fog Computing. 1-5.
Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., & Vasilakos, A. V. (2017). Fog computing for sustainable smart cities: A survey. ACM Computing Surveys (CSUR), 50(3), 1-43.
Sarkar, S., & Misra, S. (2016). Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. Iet Networks, 5(2), 23-29.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
Su, P.-C., Tan, S.-Y., Liu, Z., & Yeh, W.-C. (2022). A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system. Applied Soft Computing, 131, 109807.
Tasiopoulos, A., Ascigil, O., Psaras, I., Toumpis, S., & Pavlou, G. J. I. T. o. S. C. (2019). FogSpot: Spot Pricing for Application Provisioning in Edge/Fog Computing.
Van Veldhuizen, D. A. (1999). Multiobjective evolutionary algorithms: classifications, analyses, and new innovations.
Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM computer communication Review, 44(5), 27-32.
Yannuzzi, M., Milito, R., Serral-Gracià, R., Montero, D., & Nemirovsky, M. (2014). Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).
Yeh, W.-C. (2019). A novel boundary swarm optimization method for reliability redundancy allocation problems. Reliability Engineering & System Safety, 192, 106060.
Yeh, W.-C. (2021). One-batch Preempt Deterioration-effect Multi-state Multi-rework Network Reliability Problem and Algorithms. arXiv preprint arXiv:2103.04325.
Yeh, W.-C., Lin, Y.-P., Liang, Y.-C., Lai, C.-M., & Huang, C.-L. (2023). Simplified swarm optimization for hyperparameters of convolutional neural networks. Computers & Industrial Engineering, 177, 109076.
Yeh, W.-C., Liu, Z., Yang, Y.-C., & Tan, S.-Y. (2022). Solving dual-channel supply chain pricing strategy problem with multi-level programming based on improved simplified swarm optimization. Technologies, 10(3), 73.
Yeh, W.-C., Su, Y.-Z., Gao, X.-Z., Hu, C.-F., Wang, J., & Huang, C.-L. (2021). Simplified swarm optimization for bi-objection active reliability redundancy allocation problems. Applied Soft Computing, 106, 107321.
Yeh, W.-C., & Tan, S.-Y. (2021). Simplified swarm optimization for the heterogeneous fleet vehicle routing problem with time-varying continuous speed function. Electronics, 10(15), 1775.
Yeh, W.-C., Zhu, W., Yin, Y., & Huang, C.-L. (2023). Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization. Applied Sciences, 13(4), 2077.
Yeh, W.-C. J. E. S. w. A. (2009). A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. 36(5), 9192-9200.
Yeh, W. C. (2012). Novel swarm optimization for mining classification rules on thyroid gland data. Information Sciences, 197, 65-76.
Yeh, W. C. (2017). A new exact solution algorithm for a novel generalized redundancy allocation problem. Information Sciences, 408, 182-197.
Yeh, W. C. (2011). Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 42(1), 250-261.
Yeh, W. C. (2014). Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 64, 1-12.
Yin, Y. (2018). Multi-objective Task Scheduling in Cloud Environment Using Multi-objective Simplified Swarm Optimization. National Tsin Hua University. https://hdl.handle.net/11296/54qc4f
Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and evolutionary computation, 1(1), 32-49.
Bitam, S., Zeadally, S., & Mellouk, A. (2018). Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems, 12(4), 373-397.
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012, August). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13-16).
Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things journal, 3(6), 854-864.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
Czyzżak, P., & Jaszkiewicz, A. (1998). Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization. Journal of multi‐criteria decision analysis, 7(1), 34-47.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE internet of things journal, 3(6), 1171-1181.
Doğan, A., & Özgüner, F. (2005). Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems. The Computer Journal, 48(3), 300-314.
Fard, H. M., Prodan, R., Barrionuevo, J. J. D., & Fahringer, T. (2012). A multi-objective approach for workflow scheduling in heterogeneous environments. Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on,
Fieldsend, J. E., & Singh, S. (2002). A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence.
Han, K. K., Xie, Z. P., & Lv, X. (2018). Fog computing task scheduling strategy based on improved genetic algorithm. Computer Science, 4, 22.
He, J., Cheng, P., Shi, L., Chen, J., & Sun, Y. (2013). Time synchronization in WSNs: A maximum-value-based consensus approach. IEEE Transactions on Automatic Control, 59(3), 660-675.
Huang, C. L., & Yeh, W. C. (2019). A new SSO-based algorithm for the bi-objective time-constrained task scheduling problem in cloud computing services. arXiv preprint arXiv:1905.04855.
Jena, R. K. (2015). Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Computer Science, 57, 1219-1227.
Jiang, Y., Liu, Z., Chen, J.-H., Yeh, W.-C., & Huang, C.-L. (2023). A novel binary-addition simplified swarm optimization for generalized reliability redundancy allocation problem. Journal of Computational Design and Engineering, 10(2), 758-772.
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1‐2), 83-97.
Li, D., & Sun, X. (2006). Nonlinear integer programming (Vol. 84). Springer Science & Business Media.
Li, X. (2003). A non-dominated sorting particle swarm optimizer for multiobjective optimization. Genetic and Evolutionary Computation Conference.
Liu, J., Luo, X. G., Zhang, X. M., Zhang, F., & Li, B. N. (2013). Job scheduling model for cloud computing based on multi-objective genetic algorithm. International Journal of Computer Science Issues (IJCSI), 10(1), 134.
Matt, C. J. B., & Engineering, I. S. (2018). Fog Computing. 1-5.
Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., & Vasilakos, A. V. (2017). Fog computing for sustainable smart cities: A survey. ACM Computing Surveys (CSUR), 50(3), 1-43.
Sarkar, S., & Misra, S. (2016). Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. Iet Networks, 5(2), 23-29.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
Su, P.-C., Tan, S.-Y., Liu, Z., & Yeh, W.-C. (2022). A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system. Applied Soft Computing, 131, 109807.
Tasiopoulos, A., Ascigil, O., Psaras, I., Toumpis, S., & Pavlou, G. J. I. T. o. S. C. (2019). FogSpot: Spot Pricing for Application Provisioning in Edge/Fog Computing.
Van Veldhuizen, D. A. (1999). Multiobjective evolutionary algorithms: classifications, analyses, and new innovations.
Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM computer communication Review, 44(5), 27-32.
Yannuzzi, M., Milito, R., Serral-Gracià, R., Montero, D., & Nemirovsky, M. (2014). Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).
Yeh, W.-C. (2019). A novel boundary swarm optimization method for reliability redundancy allocation problems. Reliability Engineering & System Safety, 192, 106060.
Yeh, W.-C. (2021). One-batch Preempt Deterioration-effect Multi-state Multi-rework Network Reliability Problem and Algorithms. arXiv preprint arXiv:2103.04325.
Yeh, W.-C., Lin, Y.-P., Liang, Y.-C., Lai, C.-M., & Huang, C.-L. (2023). Simplified swarm optimization for hyperparameters of convolutional neural networks. Computers & Industrial Engineering, 177, 109076.
Yeh, W.-C., Liu, Z., Yang, Y.-C., & Tan, S.-Y. (2022). Solving dual-channel supply chain pricing strategy problem with multi-level programming based on improved simplified swarm optimization. Technologies, 10(3), 73.
Yeh, W.-C., Su, Y.-Z., Gao, X.-Z., Hu, C.-F., Wang, J., & Huang, C.-L. (2021). Simplified swarm optimization for bi-objection active reliability redundancy allocation problems. Applied Soft Computing, 106, 107321.
Yeh, W.-C., & Tan, S.-Y. (2021). Simplified swarm optimization for the heterogeneous fleet vehicle routing problem with time-varying continuous speed function. Electronics, 10(15), 1775.
Yeh, W.-C., Zhu, W., Yin, Y., & Huang, C.-L. (2023). Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization. Applied Sciences, 13(4), 2077.
Yeh, W.-C. J. E. S. w. A. (2009). A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. 36(5), 9192-9200.
Yeh, W. C. (2012). Novel swarm optimization for mining classification rules on thyroid gland data. Information Sciences, 197, 65-76.
Yeh, W. C. (2017). A new exact solution algorithm for a novel generalized redundancy allocation problem. Information Sciences, 408, 182-197.
Yeh, W. C. (2011). Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 42(1), 250-261.
Yeh, W. C. (2014). Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 64, 1-12.
Yin, Y. (2018). Multi-objective Task Scheduling in Cloud Environment Using Multi-objective Simplified Swarm Optimization. National Tsin Hua University. https://hdl.handle.net/11296/54qc4f
Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and evolutionary computation, 1(1), 32-49.