[1] G. Ismayilov, H.R. Topcuoglu, Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing, Future Generation Computer Systems, 102 (2020) 307-322.
[2] P.S. Rawat, P. Dimri, P. Gupta, G.P. Saroha, Resource provisioning in scalable cloud using bio-inspired artificial neural network model, Applied Soft Computing, 99 (2021) 106876.
[3] A. Ramegowda, J. Agarkhed, S.R. Patil, Adaptive task scheduling method in multi-tenant cloud computing, International Journal of Information Technology, 12(4) (2020) 1093-1102.
[4] D.K. Shukla, D. Kumar, D.S. Kushwaha, Task scheduling to reduce energy consumption and makespan of cloud computing using NSGA-II, Materials Today: Proceedings, (2021).
[5] W. Shu, K. Cai, N.N. Xiong, Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing, Future Generation Computer Systems, 124 (2021) 12-20.
[6] E.H. Houssein, A.G. Gad, Y.M. Wazery, P.N. Suganthan, Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends, Swarm and Evolutionary Computation, 62 (2021) 100841.
[7] M. Adhikari, S. Nandy, T. Amgoth, Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud, Journal of Network and Computer Applications, 128 (2019) 64-77.
[8] H. Yapici, N. Cetinkaya, A new meta-heuristic optimizer: Pathfinder algorithm, Applied Soft Computing, 78 (2019) 545-568.
[9] C.R. Miranda, F.P.V.d. Campos, M.V. Ribeiro, Energy reliability in macro base stations: A feasible solution based on a type-1 Mamdani fuzzy system, Electric Power Systems Research, 195 (2021) 107126.
[10] Y. Zhang, Q. Hu, Z. Meng, A. Ralescu, Fuzzy dynamic timetable scheduling for public transit, Fuzzy Sets and Systems, 395 (2020) 235-253.
[11] C.R. Vela, S. Afsar, J.J. Palacios, I. González-Rodríguez, J. Puente, Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling, Computers & Operations Research, 119 (2020) 104931.
[12] N. Mansouri, B. Mohammad Hasani Zade, M.M. Javidi, Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory, Computers & Industrial Engineering, 130 (2019) 597-633.
[13] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F.D. Rose, R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Softw. Pract. Exper., 41(1) (2011) 23–50.
[14] Z. Jia, J. Yan, J.Y.T. Leung, K. Li, H. Chen, Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities, Applied Soft Computing, 75 (2019) 548-561.
[15] H.S. Ali, R.R. Rout, P. Parimi, S.K. Das, Real-Time Task Scheduling in Fog-Cloud Computing Framework for IoT Applications: A Fuzzy Logic based Approach, in: 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2021, pp. 556-564.
[16] M. Emin Baysal, A. Sarucan, K. Büyüközkan, O. Engin, Distributed Fuzzy Permutation Flow Shop Scheduling Problem: A Bee Colony Algorithm, in: C. Kahraman, S. Cevik Onar, B. Oztaysi, I.U. Sari, S. Cebi, A.C. Tolga (Eds.) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions, Springer International Publishing, Cham, 2021, pp. 1440-1446.
[17] K. Chrysafiadi, A Fuzzy Task Scheduling Method, in: G.A. Tsihrintzis, M. Virvou (Eds.) Advances in Core Computer Science-Based Technologies: Papers in Honor of Professor Nikolaos Alexandris, Springer International Publishing, Cham, 2021, pp. 305-323.
[18] M. Melnik, D. Nasonov, Workflow scheduling using Neural Networks and Reinforcement Learning, Procedia Computer Science, 156 (2019) 29-36.
[19] M. Sharma, R. Garg, An artificial neural network based approach for energy efficient task scheduling in cloud data centers, Sustainable Computing: Informatics and Systems, 26 (2020) 100373.
[20] J.P.B. Mapetu, Z. Chen, L. Kong, Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing, Applied Intelligence, 49(9) (2019) 3308-3330.
[21] R. Medara, R.S. Singh, Amit, Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization, Simulation Modelling Practice and Theory, 110 (2021) 102323.
[22] W. Chen, E. Deelman, Workflowsim: A toolkit for simulating scientific workflows in distributed environments, in: 2012 IEEE 8th international conference on E-science, IEEE, 2012, pp. 1-8.
[23] S.A. Alsaidy, A.D. Abbood, M.A. Sahib, Heuristic initialization of PSO task scheduling algorithm in cloud computing, Journal of King Saud University - Computer and Information Sciences, (2020).
[24] M.S. Sanaj, P.M. Joe Prathap, An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment, Materials Today: Proceedings, 37 (2021) 3199-3208.
[25] Y. Gu, C. Budati, Energy-aware workflow scheduling and optimization in clouds using bat algorithm, Future Generation Computer Systems, 113 (2020) 106-112.
[26] M. Adhikari, T. Amgoth, S.N. Srirama, Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach, Applied Soft Computing, 93 (2020) 106411.
[27] S. Su, H. Yu, Minimizing tardiness in data aggregation scheduling with due date consideration for single-hop wireless sensor networks, Wireless Networks, 21(4) (2015) 1259-1273.
[28] D.E. Akyol, G.M. Bayhan, Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach, The International Journal of Advanced Manufacturing Technology, 37(5-6) (2008) 576-588.
[29] D. Alboaneen, H. Tianfield, Y. Zhang, B. Pranggono, A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers, Future Generation Computer Systems, 115 (2021) 201-212.
[30] L.A. Zadeh, Fuzzy sets, in: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, World Scientific, 1996, pp. 394-432.
[31] L.-X. Wang, Adaptive fuzzy systems and control: design and stability analysis, Prentice-Hall, Inc., 1994.
[32] L.S. Riza, C. Bergmeir, F. Herrera, J.M. Benítez, frbs: Fuzzy rule-based systems for classification and regression in R, Journal of statistical software, 65(6) (2015) 1-30.
[33] A. Arslan, M. Kaya, Determination of fuzzy logic membership functions using genetic algorithms, Fuzzy Sets and Systems, 118(2) (2001) 297-306.
[34] N.E. Nawa, T. Furuhashi, Fuzzy system parameters discovery by bacterial evolutionary algorithm, IEEE Transactions on Fuzzy Systems, 7(5) (1999) 608-616.
[35] M. Babanezhad, A.T. Nakhjiri, A. Marjani, M. Rezakazemi, S. Shirazian, Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature, Scientific Reports, 10(1) (2020) 1-13.
[36] H.N. Ghafil, K. Jármai, Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications, Applied Soft Computing, 93 (2020) 106392.