An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems

Iman Ahmadianfar (), Bijay Halder, Salim Heddam, Leonardo Goliatt, Mou Leong Tan, Zulfaqar Sa’adi, Zainab Al-Khafaji, Raad Z. Homod, Tarik A. Rashid and Zaher Mundher Yaseen ()
Additional contact information
Iman Ahmadianfar: Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan 6361663973, Iran
Bijay Halder: Department of Remote Sensing and GIS, Vidyasagar University, Midnapore 721102, India
Salim Heddam: Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route El Hadaik, BP 26, Skikda 21024, Algeria
Leonardo Goliatt: Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, Brazil
Mou Leong Tan: GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Minden, Malaysia
Zulfaqar Sa’adi: Centre for Environmental Sustainability and Water Security (IPASA), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Sekudai 81310, Johor, Malaysia
Zainab Al-Khafaji: Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah 51001, Iraq
Raad Z. Homod: Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basrah 61004, Iraq
Tarik A. Rashid: Department of Computer Science and Engineering, University of Kurdistan Helwer, Erbil 44001, Iraq
Zaher Mundher Yaseen: Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Sustainability, 2023, vol. 15, issue 3, 1-28

Abstract: Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems.

Keywords: water engineering problems; Runge–Kutta optimization; hydropower multireservoir; Muskingum model; sequential quadratic programming (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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