Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Application of Self-Adaptive Meta-Heuristic Optimization Algorithm for Muskingum Flood Routing

Muskingum 홍수추적을 위한 자가적응형 메타 휴리스틱 알고리즘의 적용

  • Lee, Eui Hoon (School of Civil Engineering, Chungbuk National University)
  • 이의훈 (충북대학교 토목공학부)
  • Received : 2020.04.24
  • Accepted : 2020.07.03
  • Published : 2020.07.31
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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.

과거 자연현상에서 발생하는 복잡한 비선형성에 따른 문제를 해결하기 위해 메타 휴리스틱 최적화 알고리즘들이 개발되었고 개발된 알고리즘의 적용성을 검토하기 위해 다양한 연구들이 진행되었다. Self-adaptive vision correction algorithm (SAVCA)는 수학 문제에서는 우수한 성능을 보여주었지만 복잡한 공학 문제들에 적용되지 않았을 뿐만 아니라 SAVCA의 적용과정에 대한 검토가 필요하다. SAVCA의 공학 문제에 대한 적용 및 적용과정에 대한 검토를 위해 최근 개발되어 우수한 성능을 보여주었던 advanced nonlinear Muskingum flood routing model (ANLMM-L)에 적용하였다. 먼저 SAVCA에 의해 초기 해집합을 생성한 후 ANLMM-L을 통해 적합도를 산출하였다. 국지탐색 및 전역탐색에 의해 선택된 새로운 값을 SAVCA에 넣고 새로운 해를 생성한 후 다시 ANLMM-L을 적용하여 적합도를 계산하였다. 새로운 해와 기존 해집합의 결과를 비교하여 개량하는 방법을 통해 마지막 연산이 진행되었다. 관측 유출량과 계산된 유출량과의 오차를 계산하기 위해 sum of squares (SSQ)가 사용되었으며 적용한 결과는 기존 방법들과 비교하였다. Muskingum 홍수추적에서 우수한 성능을 보여준 SAVCA는 다양한 공학 문제들에 적용되어 우수한 성능을 보여줄 것으로 예상된다.

Keywords

References

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