A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization

Mathematical Problems in Engineering ◽

10.1155/2020/3094941 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Maoqing Zhang ◽

Lei Wang ◽

Zhihua Cui ◽

Jiangshan Liu ◽

Dong Du ◽

...

Keyword(s):

Genetic Algorithm ◽

Topology Optimization ◽

Network Topology ◽

Optimization Problems ◽

Selection Strategy ◽

Nsga Ii ◽

Comparison Results ◽

Practical Engineering ◽

Network Topology Optimization ◽

Nondominated Sorting

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

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Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

Symmetry ◽

10.3390/sym12020260 ◽

2020 ◽

Vol 12 (2) ◽

pp. 260 ◽

Cited By ~ 1

Author(s):

Radosław Winiczenko ◽

Krzysztof Górnicki ◽

Agnieszka Kaleta

Keyword(s):

Genetic Algorithm ◽

Diffusion Coefficient ◽

Biot Number ◽

Moisture Diffusion ◽

Absolute Error ◽

Mass Diffusion ◽

Precise Determination ◽

Nsga Ii ◽

Moisture Diffusion Coefficient ◽

Nondominated Sorting

A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s2 + 0.000024316T2 − 0.12478256sT, D = 1.27547936∙10−7 − 2.3808∙10−5s − 5.08365633∙10−9T + 0.0030005179s2 + 4.266495∙10−11T2 + 8.33633∙10−7sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s2 + 0.000024316T2 − 0.12478252sT, D = 1.27547948∙10−7 − 2.3806∙10−5s − 5.08365753∙10−9T + 0.0030005175s2 + 4.266493∙10−11T2 + 8.336334∙10−7sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

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Single-reservoir operating rules for a year using multiobjective genetic algorithm

Journal of Hydroinformatics ◽

10.2166/hydro.2008.019 ◽

2008 ◽

Vol 10 (2) ◽

pp. 163-179 ◽

Cited By ~ 31

Author(s):

Taesoon Kim ◽

Jun-Haeng Heo ◽

Deg-Hyo Bae ◽

Jin-Hoon Kim

Keyword(s):

Genetic Algorithm ◽

Piecewise Linear ◽

Search Space ◽

Optimization Method ◽

Optimal Number ◽

Current Status ◽

Nsga Ii ◽

Multiobjective Genetic Algorithm ◽

Operating Rules ◽

Operating Rule

A monthly operating rule for single-reservoir operation is developed in this study. Synthetic inflow data over 100 years are generated by using a time series model, AR(1), and piecewise-linear operating rules consisting of 4 and 5 linear lines are found using the implicit stochastic optimization method. In order to consider multiobjective functions in reservoir system operation, a multiobjective genetic algorithm (NSGA-II) is adopted to obtain the optimization results. The search space of NSGA-II is carefully refined using frequency analysis of historical data, and the relationship between inflow and constraints is also investigated. It is determined that 4 and 5 segments are the optimal number of segments for the piecewise-linear operating rule, and the effect of random number seeding on NSGA-II is evaluated. Six years of historical inflow data are used for the simulation model and the results show that the developed operating rule would handle various inflow series. As a result, probabilistic reservoir storage forecasts can be provided to a system operator so as to enable the operator to evaluate the current status of a reservoir quantitatively.

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Multiobjective Optimal Design of Heat Exchanger Networks Using New Adaptations of the Elitist Nondominated Sorting Genetic Algorithm, NSGA-II

Industrial & Engineering Chemistry Research ◽

10.1021/ie070805g ◽

2008 ◽

Vol 47 (10) ◽

pp. 3489-3501 ◽

Cited By ~ 23

Author(s):

Aaditya Agarwal ◽

Santosh K. Gupta

Keyword(s):

Genetic Algorithm ◽

Heat Exchanger ◽

Optimal Design ◽

Nsga Ii ◽

Heat Exchanger Networks ◽

Nondominated Sorting

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Exploring hydrologic post-processing of ensemble stream flow forecasts based on Affine kernel dressing and Nondominated sorting genetic algorithm II

10.5194/hess-2020-238 ◽

2020 ◽

Author(s):

Jing Xu ◽

François Anctil ◽

Marie-Amélie Boucher

Keyword(s):

Genetic Algorithm ◽

Initial Conditions ◽

Post Processing ◽

Nsga Ii ◽

Underlying Distribution ◽

Predictive Skill ◽

Chaotic Nature ◽

Pareto Fronts ◽

Nondominated Sorting ◽

Different Sources

Abstract. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical 5 system and to capture the uncertainties associated with different sources. However, space still exists for improving their predictive skill and credibility through proper hydrologic post-processing. We tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II). Those two methods are theoretically/technically distinct, yet however, share the same feature that both of them relax the parametric assumption of the underlying distribution of the data (i.e., streamflow ensemble forecast). AKD transformed ensemble and the Pareto fronts 10 generated with NSGA-II demonstrated the superiority of post-processed ensemble in efficiently eliminating forecast biases and maintaining a proper dispersion with the increasing forecasting horizon.

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Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

Modelling and Simulation in Engineering ◽

10.1155/2017/2034907 ◽

2017 ◽

Vol 2017 ◽

pp. 1-17 ◽

Cited By ~ 4

Author(s):

Vimal Savsani ◽

Vivek Patel ◽

Bhargav Gadhvi ◽

Mohamed Tawhid

Keyword(s):

Heat Transfer ◽

Genetic Algorithm ◽

Multiobjective Optimization ◽

Pareto Front ◽

Search Algorithm ◽

Extreme Points ◽

Search Technique ◽

Nsga Ii ◽

Linear Ordinary Differential Equations ◽

Nondominated Sorting

Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II) for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed) set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA), and combined PSO-GA based MOEA.

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