Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm

Chen, Hai tao; Wang, Wenchuan; Chau, Kwok Wing; Xu, Lei; He, Ji

doi:10.1007/s11269-021-03005-z

Cited by 21 publications

(3 citation statements)

References 41 publications

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“…Based on this, a method based on SPM chaotic mapping is proposed, which can effectively enhance the diversity and randomness of the population, thus improving the algorithm's optimization and convergence speed. As evidenced by literature 29 , SPM chaotic mapping demonstrates strong search capabilities and enriches the diversity of the population during initialization. The formula is as follows: where n ∈ (0, 1), u ∈ (0, 1), the system is in a chaotic state, and r is a random number between 0 and 1.…”

Section: Adaptive ε-Constrained Iwoa Algorithmmentioning

confidence: 91%

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

He,

Tang,

Guo

et al. 2024

Sci Rep

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Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work.

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Section: Adaptive ε-Constrained Iwoa Algorithmmentioning

confidence: 91%

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

He,

Tang,

Guo

et al. 2024

Sci Rep

Self Cite

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“…He proposed an improved chaotic particle swarm algorithm 10 based on logistic mapping to solve the reservoir flood control scheduling model. Chen coupled the Yin-Yang Firefly Algorithm algorithm 11 and ε-constraint method to establish the three-reservoirs flood control scheduling model. Liu 12 uses particle swarm optimization algorithm with coupled penalty function to solve the maximum peak clipping model of cascade reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Study on reservoir optimal operation based on coupled adaptive ε constraint and multi strategy improved Pelican algorithm

He,

Guo,

Wang

et al. 2023

Sci Rep

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The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs.

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“…However, existing studies on the optimal dispatching of hydraulic structures mainly focus on flood control projects [7][8][9][10], hydroelectric projects [11][12][13], water distribution systems [14], reservoir operation decisions [15], and multi-objective water transfer projects [16]. Water diversion projects for improving water quality mainly adopt an empirical method or an exhaustive method [17,18] to determine a feasible water diversion scheme, which is difficult to obtain better environmental and economic benefits from.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Model and Application of Hydraulic Structure Regulation to Improve Water Quality in Plain River Networks

Huang,

Zhang,

et al. 2023

Water

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The proper dispatching of hydraulic structures in water diversion projects is a desirable way to maximize project benefits. This study aims to provide a reliable, optimal scheduling model for hydraulic engineering to improve the regional water environment. We proposed an improved gravitational search algorithm (IPSOGSA) based on multi-strategy hybrid technology to solve this practical problem. The opposition-based learning strategy, elite mutation strategy, local search strategy, and co-evolution strategies were employed to balance the exploration and exploitation of the algorithm through the adaptive evolution of the elite group. Compared with several other algorithms, the preponderance of the proposed algorithm in single-objective optimization problems was demonstrated. We combined the water quality mechanism model, an artificial neural network (ANN), and the proposed algorithm to establish the optimal scheduling model for hydraulic structures. The backpropagation neural network (IGSA-BPNN) trained by the improved algorithm has a high accuracy, with a coefficient of determination (R2) over 0.95. Compared to the two traditional algorithms, the IGSA-BPNN model was, respectively, improved by 1.5% and 0.9% on R2 in the train dataset, and 1.1% and 1.5% in the test dataset. The optimal scheduling model for hydraulic structures led to a reduction of 46~69% in total power consumption while achieving the water quality objectives. With the lowest cost scheme in practice, the proposed intelligent scheduling model is recommended for water diversion projects in plain river networks.

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Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm

Cited by 21 publications

References 41 publications

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

Study on reservoir optimal operation based on coupled adaptive ε constraint and multi strategy improved Pelican algorithm

An Optimal Model and Application of Hydraulic Structure Regulation to Improve Water Quality in Plain River Networks

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