PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH¹

Abstract

A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.

A Hybrid Search Method Based on the Artificial Bee Colony Algorithm

“…이 탐색 법은 선택된 주요 변수들을 이용하여 국부해에 빠지지 않고, 효율 적으로 전역해를 찾을 수 있다. 최근, 조화탐색법이 다른 알고리즘 들에 비해 더 빠른 수렴이 가능하다는 점을 확인하였고 [10] , 트러스 (truss) 구조물의 단면 최적화 [9] , 비선형 머스킹검 모델(nonlinear Muskingum model)의 변수 최적화 [11] , 다중 패스 정면 밀링(multipass face-milling)의 변수 최적화 [12] …”

Harmony Search for Generalized Orienteering Problem: Best Touring in China

“…The HS algorithm has been successfully applied to various benchmarking and realworld problems including traveling salesperson problem [10], parameter optimization of river flood model [11], design of pipeline network [12], and design of truss structures [13]. Consequently, the HS algorithm provides a possibility of success in a TSPlike NP-hard problem.…”

Seismic-Reliability-Based Optimal Layout of a Water Distribution Network

“…The proposed WDS optimal design model maximizes the system's seismic reliability with constraints on economic cost and pressure requirements ( Figure 1 and Section 2.5). Harmony search algorithm (HSA) is used for optimization ( Figure 1) [38,39]. In the last section of the Methodology, Todini [40]'s resilience indicator is defined.…”