Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

Hojjati, Ali; Monadi, Mohsen; Faridhosseini, Alireza; Mohammadi, Mirali

doi:10.2478/johh-2018-0006

Cited by 73 publications

(26 citation statements)

References 22 publications

(16 reference statements)

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“…It also can assess optimal quantities and reduce the time needed to reach the optimal quantity of decision variables [14]. The upgraded version of these GAs, NSGA-II, becomes the most popular method among multi-objective optimization by EAs, and the potential has been proved by ([10]; [7]; [2]) in their study.…”

Section: Non-dominated Sorting Genetic Algorithm (Nsga-ii)mentioning

confidence: 99%

Comparative study on the reservoir operation planning with the climate change adaptation

Tukimat

Harun

2019

SN Appl. Sci.

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The management planning of Pedu-Muda reservoir, Kedah, was investigated in the context of the climate change evolution. The aim of this study was to evaluate the impact of the climate change to the reservoir operating management system and its sustainability. The study was divided into two sections; Analysis 1 refers to the reservoir optimization adapted with the climate assessment. The statistical downscaling model reacted as the climate model to generate the long-term pattern of the local climates affected by the greenhouse gases. Analysis 2 refers to the reservoir optimization but excluded the climate changes assessment in the analyses. The non-dominated sorting genetic algorithm version II (NSGA-II) was applied in both analyses to optimize the water use due to the multi-objectives demand, maximizing water release, minimizing water shortage and maximizing reservoir storage. The formation of Pareto optimal solutions from both analyses was measured and compared. The results showed the Analysis 1 potential to produce consistence monthly flow with lesser error and higher correlation values. It also produced better Pareto optimal solution set and considered all the objectives demands. The NSGA-II also successfully improves and re-manages the reservoir storage efficiently and reduce the dependancy of these reservoirs.

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Section: Non-dominated Sorting Genetic Algorithm (Nsga-ii)mentioning

confidence: 99%

Comparative study on the reservoir operation planning with the climate change adaptation

Tukimat

Harun

2019

SN Appl. Sci.

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“…NSGA-II [29] is an efficient multi-objective optimization algorithm and has a wide range of applications in reservoir dispatch [8,30]. In this paper, the algorithm of NSGA-II is selected and solved based on the multi-objective optimization platform developed by Tian [31].…”

Section: Model Solutionmentioning

confidence: 99%

“…Braga et al [7] applied a combination of simulation and optimization to realize the tradeoffs between flood control and hydropower generation. Hojjati et al [8] established a multi-objective optimal dispatch model for reservoir systems with maximum generation capacity and flood storage volume. Ngo et al [9] applied a combination of simulation and optimization to realize the tradeoffs between flood control and hydropower generation, to guide the operation of reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Competitive Relationship between Flood Control and Power Generation with Flood Season Division: A Case Study in Downstream Jinsha River Cascade Reservoirs

Yao

Dong

Jia

et al. 2019

Water

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The lower reaches of Jinsha River host the richest hydropower energy sources in China. With the construction of Wudongde and Baihetan, the multi-objective optimization for cascade reservoirs (along with Xiluodu and Xiangjia Dam) in the lower reaches of Jinsha River will create significant benefits. This paper focuses on the competitive relationship between flood control and power generation, and attaches attention to the measurement of different objective functions and their competitive relationship. With observations of the flood in 1974, 1981, and 1985, a 100-year return period flood with peak-3d volume pair as different inputs for the optimal model is approached by NSGA-II. Different flood seasons divided by flood feature is applied to figure out specific competitive relationship. The results can be concluded as the following: (1) Strong competitive relationship mainly occurs in pre-flood season. (2) Whether it shows a strong competitive relationship depends on the amount of discharge. If the turbine is set to full capacity, power generation is fulfilled certainly, which means that there exists a weak competitive relationship between multi-objectives. (3) The different processes of floods have an effect on the duration of a competitive relationship. A flood with a late peak causes the extension of strong competition in the pre-flood season, which lends itself to a strong competition relationship in the post-flood season. (4) The intensity of competition in the pre-flood season is higher than that in the post-flood season because it has a larger range.

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“…Coello and Lechuga [21] presented a proposal first time to make enable PSO to deal with the multi-objective problem. Patel and Rao [22] applied multiobjective particle swarm optimization (MOPSO) algorithm to the shell and tube heat exchanger, and Hojjati et al [23] implemented MOPSO for the optimal operation of two reservoirs constructed on Ozan River catchment in order to maximize income from power generation and flood control capacity. Alrasheed [24] implemented modified versions of MOPSO to improve its convergence rate and validated its performance through benchmark functions.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Sharma

Baloni

2020

SN Appl. Sci.

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A high-bypass turbofan engine transfers air from low-to the high-pressure compressor through an S-shaped transition duct. Minimization of the total pressure loss and maximization of uniform flow are key factors to ensure the maximum performance of the S-shaped transition duct. The conventional design approach is time-consuming and does not guarantee an optimal solution. Hence, the present article is based on the application of optimization for the S-shaped compressor transition duct. The optimization is carried out on the basis of shape parameters to minimize objectives namely pressure loss coefficient and non-uniformity with the reduction of length of the S-shaped transition duct. The 2-D axisymmetric approach of computational fluid dynamics is used as a design tool for performance evaluation with optimization techniques. The simulation model is validated with the available experimental results of the literature. The correlation between the response and independent variables is established with the help of the second-order polynomial response surface methodology. Further, individual optimization is carried out for single-objective consideration using particle swarm optimization and gray wolf optimization algorithms. As the results of single-objective optimization depict the conflicts between both objectives, later optimization using multi-objective particle swarm optimization and multi-objective gray wolf optimization algorithms is carried out. The performance metrics are obtained and compared. The 'TOPSIS' method is applied to get best solution out of multiple solutions. The optimized duct reduced pressure loss coefficient and non-uniformity index by 28.14% and 43.33% with a reduction of 6.37% of length compared to baseline S-shaped duct.

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Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

Cited by 73 publications

References 22 publications

Comparative study on the reservoir operation planning with the climate change adaptation

Comparative study on the reservoir operation planning with the climate change adaptation

Competitive Relationship between Flood Control and Power Generation with Flood Season Division: A Case Study in Downstream Jinsha River Cascade Reservoirs

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

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