Optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Lévy flight and distribution enhancement technique

References

• Ahmed, A. N., Van Lam, T., Hung, N. D., Van Thieu, N., Kisi, O., & El-Shafie, A. (2021). A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Applied Soft Computing, 105, 107282. https://doi.org/https://doi.org/10.1016/j.asoc.2021.107282
   Web of Science ®Google Scholar
• Al-Aqeeli, Y. H., & Mahmood Agha, O. M. A. (2020). Optimal operation of multi-reservoir system for hydropower production using particle Swarm optimization algorithm. Water Resources Management, 34(10), 3099–3112. https://doi.org/https://doi.org/10.1007/s11269-020-02583-8
   Web of Science ®Google Scholar
• Ali, N., Othman, M. A., Husain, M. N., & Misran, M. H. (2014). A review of firefly algorithm. ARPN Journal of Engineering and Applied Sciences, 9(10).
   Google Scholar
• Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, & El-Shafie, Ahmed. (2019). Novel reservoir system simulation procedure for gap minimization between water supply and demand. Journal of Cleaner Production, 206, 928–943. https://doi.org/http://dx.doi.org/10.1016/j.jclepro.2018.09.237
   Web of Science ®Google Scholar
• Amirkhani, M., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Loáiciga, H. A. (2016). Multiobjective reservoir operation for water quality optimization. Journal of Irrigation and Drainage Engineering, 142(12), 04016065. https://doi.org/https://doi.org/10.1061/(ASCE)IR.1943-4774.0001105
   Google Scholar
• Ashofteh, P.-S., Haddad, O. B., Akbari-Alashti, H., & Mariño, M. A. (2015). Determination of irrigation allocation policy under climate change by genetic programming. Journal of Irrigation and Drainage Engineering, 141(4), 04014059. https://doi.org/https://doi.org/10.1061/(ASCE)IR.1943-4774.0000807
   Google Scholar
• Aydodu, I., Akin, A., & Saka, M. P. (2016). Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Advances in Engineering Software. https://doi.org/https://doi.org/10.1016/j.advengsoft.2015.10.013.
   Google Scholar
• Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and Information Science. https://doi.org/https://doi.org/10.5539/cis.v3n1p180.
   Google Scholar
• Ben Alaya, A., Souissi, A., Tarhouni, J., & Ncib, K. (2003). Optimization of Nebhana reservoir water allocation by stochastic dynamic programming. Water Resources Management, 17(4), 259–272. https://doi.org/https://doi.org/10.1023/A:1024721507339
   Google Scholar
• Biswas, A., Mishra, K. K., Tiwari, S., & Misra, A. K. (2013). Physics-inspired optimization algorithms: A survey. Journal of Optimization. https://doi.org/https://doi.org/10.1155/2013/438152.
   Google Scholar
• Chen, H., Xu, Y., Wang, M., & Zhao, X. (2019). A balanced whale optimization algorithm for constrained engineering design problems. Applied Mathematical Modelling. https://doi.org/https://doi.org/10.1016/j.apm.2019.02.004.
   Google Scholar
• Chia, M. Y., Huang, Y. F., & Koo, C. H. (2021). Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine. Agricultural Water Management, 243. https://doi.org/https://doi.org/10.1016/j.agwat.2020.106447.
   Google Scholar
• Chong, K. L., Lai, S. H., Ahmed, A. N., Wan Zurina, W. Z., & El-Shafie, A. (2021). Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm. Applied Soft Computing, 106, 107325. https://doi.org/https://doi.org/10.1016/j.asoc.2021.107325
   Web of Science ®Google Scholar
• Chong, K. L., Lai, S. H., Ahmed, A. N., Wan Zurina, W. Z., Rao, R. V., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2021). Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms. IEEE Access. https://doi.org/https://doi.org/10.1109/ACCESS.2021.3054424.
   Google Scholar
• Dashti Latif, S., Najah Ahmed, A., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2020). Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Engineering Journal. https://doi.org/https://doi.org/10.1016/j.aej.2020.10.057.
   Google Scholar
• Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software. https://doi.org/https://doi.org/10.1016/j.advengsoft.2017.05.014.
   Google Scholar
• Emami, Mohammad, Nazif, Sara, Mousavi, Sayed-Farhad, Karami, Hojat, & Daccache, Andre. (2021). A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation. Journal of Environmental Management, 286, 112250. https://doi.org/http://dx.doi.org/10.1016/j.jenvman.2021.112250
   PubMed Web of Science ®Google Scholar
• Faris, H, Aljarah, I, Mirjalili, S, Castillo, P.A, & Merelo, J.J. (2016). IJCCI 2016. Proceedings of the 8th International Joint Conference on Computational Intelligence, 1. https://doi.org/https://doi.org/10.5220/0006048201710177.
   Google Scholar
• Feng, Z.-k., Liu, S., Niu, W.-j., Li, S.-s., Wu, H.-j., & Wang, J.-y. (2020). Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation. Journal of Hydrology, 581(August 2019). https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.124425.
   Google Scholar
• Ghalandari, M., Ziamolki, A., Mosavi, A., Shamshirband, S., Chau, K. W., & Bornassi, S. (2019). Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments. Engineering Applications of Computational Fluid Mechanics, 13(1). https://doi.org/https://doi.org/10.1080/19942060.2019.1649196.
   Google Scholar
• Heidari, A. A., & Pahlavani, P. (2017). An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Applied Soft Computing Journal. https://doi.org/https://doi.org/10.1016/j.asoc.2017.06.044.
   Google Scholar
• Ho, Y. C., & Pepyne, D. L. (2002). Simple explanation of the no-free-lunch theorem and its implications. Journal of Optimization Theory and Applications, 115(3). https://doi.org/https://doi.org/10.1023/A:1021251113462.
   Google Scholar
• Homsi, R., Shiru, M. S., Shahid, S., Ismail, T., Harun, S. B., Al-Ansari, N., Chau, K. W., & Yaseen, Z. M. (2020). Precipitation projection using a CMIP5 GCM ensemble model: A regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics, 14(1), 90–106. https://doi.org/https://doi.org/10.1080/19942060.2019.1683076
   Web of Science ®Google Scholar
• Hossain, M.S. (2013). Adopting Artificial Intelligences in Optimizing Reservoir Operation Policy. (Call No. TD395.M837 2013) [Doctoral Thesis, Universiti Kebangsaan Malaysia, UKM, Bangi]. UKM Open Access Thesis and Dissertations.
   Google Scholar
• Hossain, M. S., & El-Shafie, A. (2014). Evolutionary techniques versus swarm intelligences: Application in reservoir release optimization. Neural Computing and Applications, 24(7–8), 1583–1594. https://doi.org/https://doi.org/10.1007/s00521-013-1389-8
   Google Scholar
• Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence. https://doi.org/https://doi.org/10.1016/j.engappai.2020.103731.
   Google Scholar
• Jalali, M. R., Afshar, A., & Mariño, M. A. (2006). Reservoir operation by ant colony optimization algorithms. Iranian Journal of Science and Technology, Transaction B: Engineering, 30(1), 107–117.
   Google Scholar
• Kamaruzaman, A. F., Zain, A. M., Yusuf, S. M., & Udin, A. (2013). Levy flight algorithm for optimization problems – A literature review. Applied Mechanics and Materials. https://doi.org/https://doi.org/10.4028/www.scientific.net/AMM.421.496.
   Google Scholar
• Kelidari, M., & Hamidzadeh, J. (2020). Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator. Soft Computing. https://doi.org/https://doi.org/10.1007/s00500-020-05349-x.
   Google Scholar
• Köppen, M., Wolpert, D. H., & Macready, W. G. (2001). Remarks on a recent paper on the “no free lunch” theorems. IEEE Transactions on Evolutionary Computation, 5(3), 295–296. https://doi.org/https://doi.org/10.1109/4235.930318
   Google Scholar
• Li, Y., Li, X., Liu, J., & Ruan, X. (2019). An improved bat algorithm based on lévy flights and adjustment factors. Symmetry. https://doi.org/https://doi.org/10.3390/sym11070925.
   Google Scholar
• Liu, Y., & Cao, B. (2020). A novel ant colony optimization algorithm with levy flight. IEEE Access. https://doi.org/https://doi.org/10.1109/ACCESS.2020.2985498.
   Google Scholar
• Ma, X. X., & Wang, J. S. (2018). An improved flower pollination algorithm to solve function optimization problem. IAENG International Journal of Computer Science, 45(3), 364–370.
   Google Scholar
• Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software. https://doi.org/https://doi.org/10.1016/j.advengsoft.2016.01.008.
   Web of Science ®Google Scholar
• Moeini, R., Soltani-nezhad, M., & Daei, M. (2017). Constrained gravitational search algorithm for large scale reservoir operation optimization problem. Engineering Applications of Artificial Intelligence, 62, 222–233. https://doi.org/https://doi.org/10.1016/j.engappai.2017.04.012
   Google Scholar
• Mohammadi, B., Linh, N. T. T., Pham, Q. B., Ahmed, A. N., Vojteková, J., Guan, Y., Abba, S. I., & El-Shafie, A. (2020). Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrological Sciences Journal, 65(10), 1738–1751. https://doi.org/https://doi.org/10.1080/02626667.2020.1758703
   Web of Science ®Google Scholar
• Mosavi, A., Shamshirband, S., Salwana, E., Chau, K. w., & Tah, J. H. M. (2019). Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Engineering Applications of Computational Fluid Mechanics, 13(1), 482–492. https://doi.org/https://doi.org/10.1080/19942060.2019.1613448
   Web of Science ®Google Scholar
• Niu, P., Wu, Z., Ma, Y., Shi, C., & Li, J. (2017). Prediction of steam turbine heat consumption rate based on whale optimization algorithm. Huagong Xuebao/CIESC Journal, 68(3). https://doi.org/https://doi.org/10.11949/j.issn.0438-1157.20161099.
   Google Scholar
• Omran, M. G. H., & Al-Sharhan, S. (2019). Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems. Engineering Applications of Artificial Intelligence. https://doi.org/https://doi.org/10.1016/j.engappai.2019.08.009.
   Google Scholar
• Qu, C., & He, W. (2015). A double mutation cuckoo search algorithm for solving systems of nonlinear equations. International Journal of Hybrid Information Technology. https://doi.org/https://doi.org/10.14257/ijhit.2015.8.12.34.
   Google Scholar
• Radosavljević, J. (2016). A solution to the combined economic and emission dispatch using hybrid PSOGSA algorithm. Applied Artificial Intelligence. https://doi.org/https://doi.org/10.1080/08839514.2016.1185860.
   Google Scholar
• Saji, Y., & Barkatou, M. (2021). A discrete bat algorithm based on Lévy flights for Euclidean traveling salesman problem. Expert Systems with Applications. https://doi.org/https://doi.org/10.1016/j.eswa.2021.114639.
   Google Scholar
• Tsipianitis, A., & Tsompanakis, Y. (2020). Improved Cuckoo search algorithmic variants for constrained nonlinear optimization. Advances in Engineering Software. https://doi.org/https://doi.org/10.1016/j.advengsoft.2020.102865.
   Google Scholar
• Wu, J., Wang, Y. G., Burrage, K., Tian, Y. C., Lawson, B., & Ding, Z. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113340.
   Google Scholar
• Wu, Y.-C., Lee, W.-P., & Chien, C.-W. (2009). Modified the performance of differential evolution algorithm with dual evolution strategy. International conference on machine learning and computing.
   Google Scholar
• Yan, G., & Li, C. (2011). An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. Journal of Computational Information Systems, 7(9), 3309–3316.
   Google Scholar
• Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. 2009 world congress on nature and biologically inspired computing, NABIC 2009 – proceedings. https://doi.org/https://doi.org/10.1109/NABIC.2009.5393690
   Google Scholar
• Zhou, Y., Ling, Y., & Luo, Q. (2018). Lévy flight trajectory-based whale optimization algorithm for engineering optimization. Engineering Computations (Swansea, Wales). https://doi.org/https://doi.org/10.1108/EC-07-2017-0264
   Google Scholar