Skip to main content

Advertisement

SpringerLink
Log in

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ahmadi M, Haddad OB, Mariño MA (2014) Extraction of flexible multi-objective real-time reservoir operation rules. Water Resour Manag 28(1):131–147

    Article  Google Scholar 

  • Akbari-Alashti H, Bozorg Haddad O, Fallah-Mehdipour E, Marino MA (2014) Multi-reservoir real-time operation rules: A new genetic programming approach. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 167, No. 10, pp. 561–576). Thomas Telford Ltd.

  • Arikoglu A (2017) Multi-objective optimal design of hybrid viscoelastic/composite sandwich beams by using the generalized differential quadrature method and the non-dominated sorting genetic algorithm II. Struct Multidiscip Optim 56(4):885–901

    Article  Google Scholar 

  • Asgari HR, Bozorg Haddad O, Pazoki M, Loáiciga HA (2015) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 142(2):04015055

    Article  Google Scholar 

  • Ashofteh PS, Haddad OB, Mariño A, M. (2012) Climate change impact on reservoir performance indexes in agricultural water supply. J Irrig Drain Eng 139(2):85–97

    Article  Google Scholar 

  • Bolouri-Yazdeli Y, Haddad OB, Fallah-Mehdipour E, Mariño MA (2014) Evaluation of real-time operation rules in reservoir systems operation. Water Resour Manag 28(3):715–729

    Article  Google Scholar 

  • Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2014) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resour Plan Manag 141(8):04014097

    Article  Google Scholar 

  • Bozorg-Haddad O, Azarnivand A, Hosseini-Moghari SM, Loáiciga HA (2016) Development of a comparative multiple criteria framework for ranking Pareto optimal solutions of a multiobjective reservoir operation problem. J Irrig Drain Eng 142(7):04016019

    Article  Google Scholar 

  • Choong SM, El-Shafie A, Mohtar WW (2017) Optimisation of Multiple Hydropower Reservoir Operation Using Artificial Bee Colony Algorithm. Water Resour Manag 31(4):1397–1411

    Article  Google Scholar 

  • Delice Y, Aydoğan EK, Özcan U, İlkay MS (2017) A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. J Intell Manuf 28(1):23–36

    Article  Google Scholar 

  • Ehteram M, Karami H, Mousavi SF, El-Shafie A, Amini Z (2017) Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowl-Based Syst 122:26–38

    Article  Google Scholar 

  • Ehteram M, Mousavi SF, Karami H, Farzin S, Singh VP, Chau KW, El-Shafie A (2018a) Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. J Hydroinf 20(2):332–355

    Article  Google Scholar 

  • Ehteram M, Karami H, Farzin S (2018b) Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm. Water Resour Manag 32(7):2315–2334

    Article  Google Scholar 

  • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. J Irrig Drain Eng 142(10):04016041

    Article  Google Scholar 

  • Haddad OB, Hosseini-Moghari SM, Loáiciga HA (2015) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resour Plan Manag 142(1):04015034

    Article  Google Scholar 

  • Jaddi NS, Alvankarian J, Abdullah S (2017) Kidney-inspired algorithm for optimization problems. Commun Nonlinear Sci Numer Simul 42:358–369

    Article  Google Scholar 

  • Johnson EG, Kathman AD, Hochmuth DH, Cook AL, Brown DR, Delaney WF (1993) Advantages of genetic algorithm optimization methods in diffractive optic design. In Diffractive and Miniaturized Optics: A Critical Review (Vol. 10271, p. 1027105). International Society for Optics and Photonics

  • Karami H, Ehteram M, Mousavi SF, Farzin S, Kisi O, El-Shafie A (2018) Optimization of energy management and conversion in the water systems based on evolutionary algorithms. Neural Computing and Applications, 1–14

  • Lingo 8.0 [Computer software] (1997) Lingo Systems, Chicago

  • Ming B, Liu P, Bai T, Tang R, Feng M (2017) Improving optimization efficiency for reservoir operation using a search space reduction method. Water Resour Manag 31(4):1173–1190

    Article  Google Scholar 

  • Moeini R, Soltani-nezhad M, Daei M (2017) Constrained gravitational search algorithm for large scale reservoir operation optimization problem. Eng Appl Artif Intell 62:222–233

    Article  Google Scholar 

  • Ren L, Zhang Y, Wang Y, Sun Z (2007) Comparative analysis of a novel M-TOPSIS method and TOPSIS. Applied Mathematics Research eXpress

  • Schardong A, Simonovic SP (2015) Coupled self-adaptive multiobjective differential evolution and network flow algorithm approach for optimal reservoir operation. J Water Resour Plan Manag 141(10):04015015

    Article  Google Scholar 

  • Tayebiyan A, Ali TAM, Ghazali AH, Malek MA (2016) Optimization of exclusive release policies for hydropower reservoir operation by using genetic algorithm. Water Resour Manag 30(3):1203–1216

    Article  Google Scholar 

  • Viteikiene M, Zavadskas EK (2007) Evaluating the sustainability of Vilnius city residential areas. J Civ Eng Manag 13(2):149–155

    Google Scholar 

  • Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika 122(6):3–6

    Article  Google Scholar 

  • Zhang Z, Jiang Y, Zhang S, Geng S, Wang H, Sang G (2014) An adaptive particle swarm optimization algorithm for reservoir operation optimization. Appl Soft Comput 18:167–177

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to appreciate so much the financial support received from the University of Malaya Research Grant (UMRG) coded RP025A-18SUS University of Malaya, Malaysia.

Author information

Authors and Affiliations

  1. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Mohammad Ehteram, Hojat Karami, Sayed Farhad Mousavi & Saaed Farzin

  2. Department of Civil Engineering, Federal University of Sergipe, São Cristóvão, Brazil

    Alcigeimes B. Celeste

  3. Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Ahmad-El Shafie

Authors
  1. Mohammad Ehteram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hojat Karami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sayed Farhad Mousavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saaed Farzin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alcigeimes B. Celeste
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ahmad-El Shafie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Ehteram.

Ethics declarations

Conflict of Interest

The authors claim that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ehteram, M., Karami, H., Mousavi, S.F. et al. Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm. Water Resour Manage 32, 4681–4706 (2018). https://doi.org/10.1007/s11269-018-2078-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-018-2078-2

Keywords

Search

Navigation