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http://dx.doi.org/10.5762/KAIS.2020.21.7.29

Application of Self-Adaptive Meta-Heuristic Optimization Algorithm for Muskingum Flood Routing  

Lee, Eui Hoon (School of Civil Engineering, Chungbuk National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.7, 2020 , pp. 29-37 More about this Journal
Abstract
In the past, meta-heuristic optimization algorithms were developed to solve the problems caused by complex nonlinearities occurring in natural phenomena, and various studies have been conducted to examine the applicability of the developed algorithms. The self-adaptive vision correction algorithm (SAVCA) showed excellent performance in mathematics problems, but it did not apply to complex engineering problems. Therefore, it is necessary to review the application process of the SAVCA. The SAVCA, which was recently developed and showed excellent performance, was applied to the advanced Muskingum flood routing model (ANLMM-L) to examine the application and application process. First, initial solutions were generated by the SAVCA, and the fitness was then calculated by ANLMM-L. The new value selected by a local and global search was put into the SAVCA. A new solution was generated, and ANLMM-L was applied again to calculate the fitness. The final calculation was conducted by comparing and improving the results of the new solution and existing solutions. The sum of squares (SSQ) was used to calculate the error between the observed and calculated runoff, and the applied results were compared with the current models. SAVCA, which showed excellent performance in the Muskingum flood routing model, is expected to show excellent performance in a range of engineering problems.
Keywords
SAVCA; Metaheuristic Optimization; Muskingum Flood Routing; Continuous Flow; ANLMM-L;
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Times Cited By KSCI : 4  (Citation Analysis)
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