scholarly journals Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Water ◽  
2018 ◽  
Vol 10 (6) ◽  
pp. 807 ◽  
Author(s):  
Mohammad Ehteram ◽  
Faridah Binti Othman ◽  
Zaher Mundher Yaseen ◽  
Haitham Abdulmohsin Afan ◽  
Mohammed Falah Allawi ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Flood Routing ◽  
Swarm Optimization ◽  
Routing Method

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

An Improved Chaotic Binary Bat Algorithm for QoS Multicast Routing

2016 ◽  
Vol 25 (04) ◽  
pp. 1650025 ◽  
Author(s):  
Yassine Meraihi ◽  
Dalila Acheli ◽  
Amar Ramdane-Cherif
Keyword(s):  
Particle Swarm Optimization ◽  
Communication Networks ◽  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Logistic Map ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Multicast Tree ◽  
Swarm Optimization ◽  
Routing Problem

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

scholarly journals Locally tuned hybridized particle swarm optimization for the calibration of the nonlinear Muskingum flood routing model

2020 ◽  
Vol 11 (S1) ◽  
pp. 343-358 ◽  
Author(s):  
Umut Okkan ◽  
Umut Kirdemir
Keyword(s):  
Particle Swarm Optimization ◽  
Initial Conditions ◽  
Particle Swarm ◽  
Solution Space ◽  
Global Solutions ◽  
Pso Algorithm ◽  
Flood Routing ◽  
Swarm Optimization ◽  
Model Benchmark ◽  
Lm Algorithm

Abstract In the literature about the parameter estimation of the nonlinear Muskingum (NL-MUSK) model, benchmark hydrographs have been subjected to various metaheuristics, and in these studies the minor improvements of the algorithms on objective functions are imposed as ‘state-of-the-art’. With the metaheuristics involving more control variables, the attempt to search global results in a restricted solution space is not actually practical. Although metaheuristics provide reasonable results compared with many derivative methods, they cannot guarantee the same global solution when they run under different initial conditions. In this study, one of the most practical of metaheuristics, the particle swarm optimization (PSO) algorithm, was chosen, and the aim was to develop its local search capability. In this context, the hybrid use of the PSO with the Levenberg–Marquardt (LM) algorithm was considered. It was detected that the hybrid PSO–LM gave stable global solutions as a result of each random experiment in the application for four different flood data. The PSO–LM, which stands out with its stable aspect, also achieved rapid convergence compared with the PSO and another hybrid variant called mutated PSO.

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