Application of Biogeography-Based Optimization (BBO) in an Optimal Operation of Reservoirs (Case Study: The Karon4 Dam)

Subject Areas : Subjects

Seyed Mohammad Hosseini-Moghari ¹ (Ph.D Student of Water Resources Engineering, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.)
Omid Bozorg-Haddad ² (استاد گروه آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج)

Received: 2014-06-16 Accepted : 2017-10-20 Published : 2017-08-23

Keywords: Optimization, Evolutionary Algorithm, biogeography-based optimization, operation of reservoir,

Abstract :

Nowadays, along with an increase in water needs, there is no balance between water demand and water supply in most regions of the country. Therefore, planning an appropriate policy to strike a balance between a declining water supply and an increasing demand to prevent a crisis is of utmost importance. The use of optimization methods in this context can be useful. Evolutionary algorithm methods are known as appropriate methods in this regard, and their suitable performance have been reported. Biogeography-based optimization (BBO) is a new evolutionary algorithm which its high performance in some aspects has been proved. The main objective of this study was to assess the performance of BBO in water resources management for the first time. Firstly, BBO was used for finding optimal points of three benchmark function including Sphere, Rosenbrock, and Bukin6; secondly, it was applied for an optimal operation of the Karon4 Reservoir with the aim of hydropower generation. In order to evaluate the performance of BBO, in addition to this method, the genetic algorithm (GA) and the nonlinear programming (NLP) were employed. The results of benchmark function showed that BBO delivered a better performance than the GA in finding the optimal points of three functions. Moreover, BBO reached an optimal solution with a higher degree of accuracy. In operation of the Karon 4 Reservoir, the results also indicated the high efficiency of BBO in extracting optimal operational policies in such circumstances; the objective function value of BBO at the best performance was 1.223, and, that for GA was 1.535. Furthermore, the global optimal solution obtained from NLP for this problem was 1.213.

References:

1)       جنت­رستمی، س.، م. خلقی و الف. بزرگ­حداد. 1389. مدیریت بهره­برداری از سدهای مخزنی با استفاده از الگوریتم اصلاح­شده جستجوی هارمونی. مجله دانش آب و خاک.  20: 61-72.

2)       عمادی، ع.، م. خادمی، س.الف. محسنی­موحد و م. نوری امامزاده­ئی. 1391. مقایسه الگوریتم بهینه­سازی بازپخت (SA)، شبیه آبدهی و سیاست بهره­برداری استاندارد در بهره­برداری از مخزن (مطالعه موردی: سد مخزنی درودزن). مجله پژوهش آب ایران. 6: 187-196.

3)       فلاح­ مهدی­پور، ا. و الف. بزرگ­حداد.1390. بهینه­سازی بهره­برداری از مخازن سدهای چند­منظوره با کاربرد روش بهینه­سازی مجموعه ذرات. مجله آب و فاضلاب.  23: 97-105.

4)       Afshar, A., O. Bozorg Haddad, M.A. Mariño, and B.J. Adams. 2007. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J. Franklin Inst. 344: 452-462.

5)       Chang, L.C., and F.J. Chang. 2009. Multi-objective evolutionary algorithm for operating parallel reservoir system. J.  Hydrol. 377: 12-20.

6)       Fallah-Mehdipour, E., O.B. Haddad, and M.A. Mariño. 2012. Real-Time operation of reservoir system by genetic programming. Water Resour. Manage. 26: 4091-4103.

 

7)       Haddad, O.B., A. Afshar, and M.A. Mariño. 2008. Design-operation of multi-hydropower reservoirs: HBMO approach. Water Resour. Manage. 22: 1709-1722.

8)       Hadidi, A., and A. Nazari. 2013. Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm. Appl. Therm. Eng. 51: 1263–1272.

9)       Jamuna, K., and K.S. Swarup. 2011. Biogeography based optimization for optimal meter placement for security constrained state estimation. Swarm Evoluti. Comput. 1: 89-96.

10)   Jothiprakash, V., and G. Shanthi. 2006. Single reservoir operating policies using genetic algorithm. Water Resour. Manage. 20: 917-929.

11)   Karamouz, M., and M.H. Houck. 1987. Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation1.  J. Am. Water Resour. Associ. 23: 1-9.

12)   Kumar, D.N., and M.J. Reddy. 2006. Ant colony optimization for multi-purpose reservoir operation. Water Resour. Manage. 20: 879-898.

13)   Mousavi, S.J., K. Ponnambalam, and F. Karray. 2005. Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach. Water Resour. Manage.  19: 655-672.

14)   Revelle, C., E. Joeres, and W. Kirby. 1969. The linear decision rule in reservoir management and design: 1, Development of the stochastic model. Water Resour. Res. 5: 767-777.

15)   Simon, D. 2008. Biogeography-based optimization. Evoluti. Comput. IEEE Trans. 12: 702-713.

16)   Simon, D., R. Rarick, M. Ergezer, and D. Du. 2011. Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms. Inform. Sci. 181: 1224-1248.

17)   Zahraie, B., and S.M. Hosseini. 2009. Development of reservoir operation policies considering variable agricultural water demands. Expert Syst. Appli. 36: 4980-4987.