Shape Optimization of Trapezoidal Labyrinth Weirs Using Genetic Algorithm

Kardan, Nazila; Hassanzadeh, Yousef; Bonab, Babak Shakooei

doi:10.1007/s13369-016-2355-4

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…It should be reminded that CMOPSO and MMOPSO are two of the most recently improved versions of MOPSO and NSGA-II recently employed by Ohadi and Jafari-Asl [14] for multi-objective optimization of Ute dam LW. The Cd of an LW plays the most critical role in the discharge calculations [1], [16]- [19]. Thus, estimating a precise Cd severely affects the optimization results.…”

Section: Resultsmentioning

confidence: 99%

“…According to (2) and earlier studies, Ht/P and α are two significant parameters affecting the Cd [16]- [19]. In a trapezoidal LW, α could be calculated as [18]:…”

Section: Labyrinth Weirmentioning

confidence: 99%

“…The ratio of labyrinth length and width in one cycle is known as the magnification ratio of the length. Kardan et al [16] suggested that in LW optimization, this ratio should be kept between 3 and 9.5. Thereby the third and fourth constraints could be defined as follow:…”

Section: Constraintsmentioning

confidence: 99%

“…They found that by employing GA and DE, the concrete consumption of LW is reduced by 19.3% and 16.6%, respectively. Later, Kardan et al [16] adopted GA to Optimize the Ute dam LW with a trapezoidal cycle shape. Their optimal solution reduced the concrete consumption of Ute LW by 21.47%.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Multi-Objective Particle Swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

et al. 2021

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Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (Cd) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

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Section: Resultsmentioning

confidence: 99%

“…According to (2) and earlier studies, Ht/P and α are two significant parameters affecting the Cd [16]- [19]. In a trapezoidal LW, α could be calculated as [18]:…”

Section: Labyrinth Weirmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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