Bi-Attempted Base Optimization Algorithm on Optimization of Hydrosystems

This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA), which has been recently developed as a numerical bidirectional search algorithm....

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Bibliographic Details
Published in:Water resources management 2023-07, Vol.37 (9), p.3585-3597
Main Authors: Yildiz, Burhan, Ulukok, Mehtap Kose, Bashiry, Vali
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric Sciences
Civil Engineering
Constraints
Convergence
Design parameters
Earth and Environmental Science
Earth Sciences
Environment
Fluid flow
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Hydraulics
Hydrogeology
Hydrology/Water Resources
Objective function
Operators (mathematics)
Optimization
Optimization algorithms
Parameters
Reservoirs
Search algorithms
Slurries
Slurry pipelines
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Summary:This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA), which has been recently developed as a numerical bidirectional search algorithm. The slurry pipeline problem is a constrained non-linear cost minimization problem with constraints on facility capacities. It has two separate cost terms that behave differently with changes in decision variables. The problem includes several decision variables in addition to the fact that the objective function is highly non-linear. On the other hand, the multi-reservoir problem is a well-known problem in Hydraulics that aims to maximize benefit by optimizing the releases of each reservoir. The problem has a known global optimum, which is used to test the abilities of the ABaOA. The ABaOA is developed from Base Optimization Algorithm (BaOA) by transforming its operators with the aim to diversify the search paths to reach the global optimum. Its applications in hydrosystems show that it converges to the optimum solutions in reasonable times. The results from the first application are compared to the ones obtained from Genetic Algorithms (GA) application. It is observed that ABaOA outperformed GA in terms of speed of convergence and finding a better alternative solution. The ABaOA reaches the global optimum in the second application. In addition, alternatives with better benefit functions, including some penalties have been determined.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-023-03517-w