Optimum outflow determination of the multi-reservoir system using constrained improved artificial bee colony algorithm
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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.
KeywordsImproved artificial bee colony algorithm Optimal operation Multi-reservoir system Exploration Exploitation
This research did not receive any specific grant from funding agencies in the public, commercial, or not for-profit sector.
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Conflict of interest
The authors declare that they have no conflict of interest statement.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Adeyemo J, Stretch D (2018) Review of hybrid evolutionary algorithms for optimizing a reservoir. S Afr J Chem Eng 25:22–31Google Scholar
- Bi X, Wang Y (2011) An improved artificial bee colony algorithm. In: 3rd international conference on computer research and development, Shanghai, ChinaGoogle 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 225–231Google 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 TechnologyGoogle 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
- 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 1:13–17Google 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 251–256CrossRefGoogle Scholar
- Sharma TK, Pant M, Singh VP (2012) Improved local search in artificial bee colony using golden section search. J Eng 1(1):14–19Google Scholar