Bi-Attempted Base Optimization Algorithm on Optimization of Hydrosystems

• Published in: Water resources management Vol. 37; no. 9; pp. 3585 - 3597
• Main Authors: Yildiz, Burhan, Ulukok, Mehtap Kose, Bashiry, Vali
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; Civil Engineering; Constraints; Convergence; Design parameters; Earth and Environmental Science; Earth Sciences; Environment; Fluid flow; fluid mechanics; 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; water; Slurry pipelines; Evolutionary algorithms; Numerical optimization; Hydraulics; Reservoir optimization; Multimodal functions
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-023-03517-w

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.