A modified water cycle algorithm for long-term multi-reservoir optimization

Xu, Yuni; Mei, Yadong

doi:10.1016/j.asoc.2018.06.031

Cited by 40 publications

(9 citation statements)

References 45 publications

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“…us, the problem faced by the decision-maker is long-term stochastic optimization. e "long-term" we mentioned here is far beyond the long-term scale considered in previous works, such as 3-5 years in Gjelsvik et al [4] or 12-period used in Cheng et al [5], Wang et al [6], and Xu and Mei [7], because of the long-lasting e ects of the location decisions. Hence, the challenge is how to handle such overlong stochastic nature.…”

Section: Introductionmentioning

confidence: 89%

“…Second, in order to address the computational complexity, numerous heuristic algorithms have been considered by sacrificing some optimality in recent years. Typical methods used in this stream include particle swarm optimization [22,23,24], electromagnetism-like mechanism [25,26], genetic algorithm [27], water cycle algorithm [7], and artificial intelligence algorithms [28]. However, all these studies focus on the deterministic setting.…”

Section: Introductionmentioning

confidence: 99%

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Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Wang

Chen

2019

Complexity

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In the location-related planning of a hydropower system, the consideration of future operations under uncertainties can make the decisions sustainable and robust. Then, it is of great importance to develop an effective approach that deals with the long-term stochasticity due to the long-lasting effects of the location selections. Thus, we propose a multistage stochastic programming model to optimize the planning decisions of cascade hydropower stations and the long-term stochastic operations in an integrated way. The first stage (i.e., the planning stage) in the model deals with the location and capacity decisions of the hydropower stations, while the subsequent stages implement the scheduling decisions under each stagewise stochastic scenario. To address the curse of dimensionality caused by the long-term stochastic operations, we further propose a novel dimensionality reduction approach based on dual equilibrium to transform the multistage model into a tractable two-stage stochastic program. The applicability of our approach is validated by a case study based on a basin of Yangtze River, China, and corresponding sensitivity analysis.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Wang

Chen

2019

Complexity

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“…Evaporation and rainfall are used to jump out of the local optical solution in this process. The advantages of the WCA have been gradually proved in the fields of mathematics [32], mechanical engineering [33], electrical engineering [34], control engineering [35], structural engineering [36], and civil engineering [37]. The flow chart of the WCA is shown in Figure 7, which shows that the calculation steps of WCA are as follows: (1) Initial parameters of WCA are set, including initial population number N pop , the number of rivers and seas N sr , the number of variables to be optimized N, upper limit UB, lower limit LB, the speed of the adjusting parameter C, the readjust range parameter u, the evaporation judge parameter d max , the maximum iterations IT max .…”

Section: Establishment Of Lhs-wca Algorithmmentioning

confidence: 99%

Calibration of Adjustment Coefficient of the Viscous Boundary in Particle Discrete Element Method Based on Water Cycle Algorithm

Gao²,

Cheng

et al. 2022

Water

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The viscous boundary has a direct influence on the accuracy of structural dynamic response analysis, and the absorbing effect of the viscous boundary is controlled by the adjustment coefficient. Therefore, a calibration model of the viscous boundary’s adjustment coefficient based on the water cycle algorithm is established for the particle discrete element to improve the accuracy of dynamic response analysis. First, the traditional viscous boundary theory is utilized to realize the viscous boundary’s application method in the particle discrete element via programming. This avoids the reflection and superposition of seismic waves at the boundary and makes the structural dynamic response with the particle discrete element more real and accurate. Second, for the complex and time-consuming adjustment coefficients determination, a calibration model based on the water cycle algorithm and Latin hypercube sampling is established for the adjustment coefficients in the particle discrete element method. Finally, this calibration model is employed for the seismic response analysis of a rockfill slope, the maximum velocity of rock in this rockfill slope being about 1.30 times that of a seismic wave. Comparing the rockfill slope response with fixed and viscous boundaries, the calibration’s accuracy and the viscous boundary’s feasibility are demonstrated, further expanding the research and application of the particle discrete element method in dynamic response analysis.

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“…A logistic chaotic map was used in the development of this new DC-WCA algorithm. Additionally, 6 benchmark functions were utilized to measure the performance of the DC-WCA method and to make comparisons between algorithms [62].…”

Section: Physics-based Algorithms and Chaosmentioning

confidence: 99%

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Atali¹,

Pehlivan²,

Gürevin³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems-where chaos theory is widely employed is optimization problems.Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process.In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed.This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In addition, PSO, FA, ABC, WO algorithms are compared in terms of their common features, and algorithms with the best success rate are presented.

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A modified water cycle algorithm for long-term multi-reservoir optimization

Cited by 40 publications

References 45 publications

Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Calibration of Adjustment Coefficient of the Viscous Boundary in Particle Discrete Element Method Based on Water Cycle Algorithm

Chaos in metaheuristic based artificial intelligence algorithms: a short review

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