Abstract
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.
Highlights
Labyrinth Weir (LW) is a linear weir that is folded in plan-view
Multi-objective problem for labyrinth weir of Ute dam was solved with a novel improved multi-objective particle swarm optimization termed TOPSIS Fuzzy MOPSO (TFMOPSO)
The proposed algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions and synchronously take advantage of a Mamdani Fuzzy Inference System (FIS) to choose two leaders among the solutions which are archived in the repository
Summary
Labyrinth Weir (LW) is a linear weir that is folded in plan-view. In this way, LW provides a longer crest length, especially when there is a limitation in channel width. The Multi-Objective Problem (MOP) of LW is defined and subsequently solved with a novel improved Multi-Objective Particle Swarm Optimization algorithm (MOPSO) termed TOPSIS Fuzzy MOPSO (TFMOPSO). Ferdowsi et al [17] investigated half-round and quarter-round crest shapes in the optimal design of Ute dam LW They proposed a hybrid bat and PSO algorithm to minimize the construction cost of LW (i.e., concrete consumption). This study extensively explains the LW MultiObjective Problem (LW-MOP) and solves it through a novel multi-objective particle swarm optimization algorithm (i.e., TFMOPSO). According to the most recent researches on LW optimization the Radial Basis Function Neural Networks is employed to estimate the Cd within the optimization process In this manner, by comparing the optimal result of this study with previous research the influence of Cd estimation method on optimal solutions could be investigated. A detailed discussion on RBFNN could be found in [34]
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