Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Shokri, Ashkan; Bozorg‐Haddad, Omid; Mariňo, Miguel A.

doi:10.1007/s11269-013-0285-4

Cited by 65 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of social economy, the reservoir urgently needs to improve the utilization of water and play its comprehensive benefit. The operation models and the optimization algorithms from single target [13,14] to multi-objective [15][16][17][18][19][20][21][22][23][24][25][26] have become the future overall trend of reservoir optimization. The operation model was based on a single-objective model of power generation originally.…”

Section: Introductionmentioning

confidence: 99%

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Bai

Liu

et al. 2020

Water

View full text Add to dashboard Cite

Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.

show abstract

Section: Introductionmentioning

confidence: 99%

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Bai

Liu

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Due to time-taking computational problem in multi-reservoir systems as well as modeling with longer time horizon, it is suggested to use meta-models such as artificial neural network (ANNs) which is less time-consuming for finding an optimal solution in complicated operation problems. For example, Shokri et al (2013) proposed that non-dominated sorting genetic algorithm-II (NSGA-II-ANN) is an evolutionary algorithm applied in multi-objective optimization problem in spill of pollution situations.…”

Section: Introductionmentioning

confidence: 99%

Cascade hydropower systems optimal operation: implications for Iran’s Great Karun hydropower systems

Jamali

2019

Appl Water Sci

View full text Add to dashboard Cite

Water resources optimal allocation in large complex water systems such as multi-reservoir dams is a challenging task for decision makers. Wide range of decision variables as well as difficult nonlinear optimization problems (highly nonlinear problems) make it difficult to use optimization approaches. Alternatively, simulation-optimization approaches are then providing more realistic and applicable solution. This study presents a simulation-optimization framework for extracting monthly long-term operation rules. This method is applied to Karun River Basin including six multi-objective (hydropower generation, agricultural and environmental water supplement) cascade dams. In this regard, Water Evaluation and Planning System is used as the simulation model coupled with an optimization model. Decision variables include (1) monthly variation of top of buffer parameter in reservoirs where the reservoir releases water to meet the required demand and (2) monthly priority for filling of the reservoirs. Two-objective NSGA-II algorithms are used to minimize sum of squares unmet energy and agricultural water demands within two scenarios. The results show that the reliability of the generation of hydroelectricity in Karun River Basin has been increased sufficiently. Results showed that the scenario 2 (an aggregate energy demand at the system level) has better performance in terms of reliability (91.4% compared to 89.6%) and efficiency of centralized approach in which all reservoirs are operated in an integrated management scheme.

show abstract

“…Resolution of relevant issues in the field of water resources systems planning and management is not easy, unless optimization models are used. These models have been widely utilized and applied in different water resources problems by various researchers, such as reservoir operation (Bozorg Haddad et al 2008;Ashofteh et al 2013Ashofteh et al , 2015a, hydraulic structures (Bozorg Haddad et al 2010), design-operation of pumped-storage and hydropower systems (Bozorg Haddad et al 2014), and development of algorithms (Shokri et al 2013;Ashofteh et al 2015b). But few optimization investigations have been conducted in the field of protection of flood areas, which has been discussed in the original paper as the main contribution of the study and it has been discussed by the discusser.…”

mentioning

confidence: 99%

Closure to “Levee Layouts and Design Optimization in Protection of Flood Areas” by Omid Bozorg Haddad, Parisa-Sadat Ashofteh, and Miguel A. Mariño

Bozorg‐Haddad

Ashofteh²

2016

J. Irrig. Drain Eng.

Self Cite

View full text Add to dashboard Cite

Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Cited by 65 publications

References 28 publications

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Cascade hydropower systems optimal operation: implications for Iran’s Great Karun hydropower systems

Closure to “Levee Layouts and Design Optimization in Protection of Flood Areas” by Omid Bozorg Haddad, Parisa-Sadat Ashofteh, and Miguel A. Mariño

Contact Info

Product

Resources

About