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Optimum outflow determination of the multi-reservoir system using constrained improved artificial bee colony algorithm

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Abstract

In this research, a new meta-heuristic algorithm, named artificial bee colony (ABC) algorithm, is used to solve multi-reservoir operation optimization problem. For this purpose, two improved versions of ABC are proposed by modifying the structure of original standard form of ABC algorithm. Furthermore, in order to increase the performance of proposed algorithms for solving large-scale problems, the constrained versions of original and improved form of ABC algorithms have been proposed in which the problem constraints are explicitly satisfied. Two benchmark text examples, including four- and ten-reservoir operation optimization problems, are solved here using proposed algorithms, and the results are presented and compared. In order to solve these problems, here, two formulations are also proposed in which in the first formulation, the water releases from the reservoir and in the second one the water storage volumes of the reservoir are considered as the decision variables of the problem. Comparison of the results shows that by using the improved ABC algorithm, the better results are obtained with less computational effort in comparison with the original form of ABC algorithm in which the result improvement is notable when the proposed constrained version of the algorithms is used.

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References

  • Adeyemo J, Stretch D (2018) Review of hybrid evolutionary algorithms for optimizing a reservoir. S Afr J Chem Eng 25:22–31

    Google Scholar 

  • Afshar MH (2013) Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system. Electr Power Energy Syst 51:71–81

    Google Scholar 

  • Afshar MH, Moeini R (2008) Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems. Water Resour Manag 22:1835–1857

    Google Scholar 

  • Afshar A, Bozorg Haddad O, Marino M, Adams ABJ (2007) Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Frankl Inst 344(5):452–462

    MATH  Google Scholar 

  • Bashiri-Atrabi H, Qaderi K, Rheinheimer D, Sharifi E (2015) Application of harmony search algorithm to reservoir operation optimization. Water Resour Manag 29(15):5729–5748

    Google Scholar 

  • Bi X, Wang Y (2011) An improved artificial bee colony algorithm. In: 3rd international conference on computer research and development, Shanghai, China

  • BozorgHadad O, Afshar A, Marino MA (2008) Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs. J Hydroinformatics 10(3):257–264

    Google Scholar 

  • Castelletti A, Pianosi F, Restelli M (2013) A multiobjective reinforcement learning approach to water resources systems operation: pareto frontier approximation in a single run. Water Resour Res 49:3476–3486

    Google Scholar 

  • Chang LC, Chang FJ, Wang KW, Dai ShY (2010) Constrained genetic algorithm for optimizing multi-use reservoir operation. J Hydrol 390:66–74

    Google Scholar 

  • Chen M (2019) Improved artificial bee colony algorithm based on escaped foraging strategy. J Chin Inst Eng 42(6):516–524

    Google Scholar 

  • Chen W, Xiao Y (2019) An improved ABC algorithm and its application in bearing fault diagnosis with EEMD. Algorithms 12(4):72

    MathSciNet  MATH  Google Scholar 

  • Esat V, Hall MJ (1994) Water resources system optimization using genetic algorithms hydro informatics. In: Proceedings of the Ist international conference on hydro informatics, Balkema, Rotterdam, The Netherlands, pp 225–231

  • Hossain MDS, EI-shafie A (2014) Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam. Neural Comput Appl 24:1199–1206

    Google Scholar 

  • Huo J, Zhang Z (2018) Application of an improved ABC algorithm in urban land use prediction. Information 9:193

    Google Scholar 

  • Jalali MR (2005) Optimal design and operation of hydro systems by ant colony algorithms: new heuristic approach. Ph.D. thesis, Department of Civil Engineering, Iran University of Science and Technology

  • Jalali MR, Afshar A, Marino MA (2007) Multi-colony ant algorithm for continuous multi-reservoir operation optimization problems. J Water Resour Res 21(9):1429–1447. https://doi.org/10.1007/s11269-006-9092-5

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    MathSciNet  MATH  Google Scholar 

  • Karami H, Farzin S, Jahangiri A, Ehteram M, Kisi O, El-Shafie A (2019) Multi-reservoir system optimization based on hybrid gravitational algorithm to minimize water-supply deficiencies. Water Resour Manag 33(8):2741–2760

    Google Scholar 

  • Kumar V, Yadav SM (2018) Optimization of reservoir operation with a new approach in evolutionary computation using TLBO algorithm and jaya algorithm. Water Resour Manag 32(13):4375–4391

    Google Scholar 

  • Larson RE (1968) State increment dynamic programming. Elsevier Science, New York

    MATH  Google Scholar 

  • Ming B, Chang J, Huang Q, Wang Y, Huang S (2015) Optimal operation of multi-reservoir system based-on cuckoo search algorithm. Water Resour Manag 29(15):5671–5687

    Google Scholar 

  • Moeini R (2014) Performance evaluation of the ant colony optimization algorithm for the optimal operation of a multi-reservoir system: comparing four algorithms. Iran Water Resour Res 11(2):29–46 (in Persian)

    Google Scholar 

  • Moeini R, Afshar MH (2013) Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems. J Hydroinf 15(1):155–173

    Google Scholar 

  • Moeini R, Babaei M (2017) Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches. Evol Syst 8(4):287–301

    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

    Google Scholar 

  • Murray DM, Yakowits S (1979) Constrained differential dynamic programming and its application to multi-reservoir control. Water Resour Reserv 15(5):1017–1027

    Google Scholar 

  • Naveena S, Malathy S, Saranya D, Kumar DR (2015) An improved artificial bee colony (IABC) algorithm for numerical function optimization. Int J Appl Inf Commun Eng 1:13–17

    Google Scholar 

  • Pian J, Wang G, Li B (2018) An improved ABC algorithm based on initial population and neighborhood search, part of special issue. In: Qin SJ, Wayne Bequette B, Biegler LT, Guay M, Findeisen R, Wang J, Zavala V (eds) 10th IFAC symposium on advanced control of chemical processes ADCHEM 2018: Shenyang, China, 25–27 July, IFAC, vol 51(18), pp 251–256

  • Rani D, Moreira MM (2010) Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24:1107–1138

    Google Scholar 

  • Reddy MJ, Kumar DN (2006) Ant colony optimization for multi-purpose reservoir operation. J Water Resour Manag 20:879–889

    Google Scholar 

  • Samadi-koucheksaraee A, Ahmadianfar I, Bozorg-Haddad O, Asghari-pari SA (2019) Gradient evolution optimization algorithm to optimize reservoir operation systems. Water Resour Manag 33(2):603–625

    Google Scholar 

  • Sharma TK, Pant M, Singh VP (2012) Improved local search in artificial bee colony using golden section search. J Eng 1(1):14–19

    Google Scholar 

  • Wang KW, Chang LC, Chang FJ (2011) Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Adv Water Resour 34:1343–1351

    Google Scholar 

  • Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. Water Resour Plan Manag 125(1):25–33

    Google Scholar 

  • Yang J, Peng Z (2018) Improved ABC algorithm optimizing the bridge sensor placement. Sensors (Basel) 18(7):2240

    Google Scholar 

  • Yasar M (2016) Optimization of reservoir operation using cuckoo search algorithm: example of Adiguzel Dam, Denizli, Turkey. Math Probl Eng 1:1–7

    Google Scholar 

  • Yaseen ZM, Falah Allawi M, Karami H, Ehteram M, Farzin S, Ahmed AN, Koting SB, Mohd NS, Jaafar WZB, Afan HA, El-Shafie A (2019) A hybrid bat–swarm algorithm for optimizing dam and reservoir operation. Neural Comput Appl 31(12):8807–8821

    Google Scholar 

  • Zarei A, Mousavi SF, Eshaghi Gordji M, Karami H (2019) Optimal reservoir operation using bat and particle swarm algorithm and game theory based on optimal water allocation among consumers. Water Resour Manag 33(9):3071–3093

    Google Scholar 

  • Zhang J, Wu ZH, Cheng CH, Zhang SH (2011) Improved particle swarm optimization algorithm for multi-reservoir system operation. Water Sci Eng 4(1):61–73

    Google Scholar 

  • Zhang X, Yu X, Qin H (2016) Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization. J Hydro Environ Res 10:50–63

    Google Scholar 

Download references

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not for-profit sector.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, 81746-73441, Isfahan, Iran

    Ramtin Moeini & Farnaz Soghrati

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  1. Ramtin Moeini
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  2. Farnaz Soghrati
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Correspondence to Ramtin Moeini.

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Moeini, R., Soghrati, F. Optimum outflow determination of the multi-reservoir system using constrained improved artificial bee colony algorithm. Soft Comput 24, 10739–10754 (2020). https://doi.org/10.1007/s00500-019-04577-0

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