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

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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HYBRID GENETIC ALGORITHM TO THE SYNTHESIS OF OPTIMAL HEAT EXCHANGER NETWORKS

Revista de Engenharia Térmica ◽

10.5380/reterm.v4i1.3546 ◽

2005 ◽

Vol 4 (1) ◽

pp. 35

Author(s):

M. A. S. S. Ravagnani ◽

A . P. Silva ◽

A. A. Constantino

Keyword(s):

Genetic Algorithm ◽

Heat Exchanger ◽

Hybrid Genetic Algorithm ◽

Second Step ◽

Pinch Analysis ◽

Pinch Point ◽

Optimal Heat ◽

Heat Exchanger Networks

In this paper a new systematic is proposed, interfacing Pinch Analysis and Genetic Algorithms (GA). Initially the optimal ∆Tmin is found by using a genetic algorithm. In a second step, with the optimal ∆Tmin, the pinch point is obtained, and the problem is divided in two regions, below and above it. The optimal HEN is obtained for each side of the pinch and the final HEN is achieved. An example from the literature was solved using the proposed systematic. Results show the applicability of the proposed methodology, obtaining a cost value lower than those presented in the literature.

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Optimal Design of Heat Exchanger Networks by Using SQP Algorithm Based on GPU Acceleration

Computer Aided Chemical Engineering - 24th European Symposium on Computer Aided Process Engineering ◽

10.1016/b978-0-444-63456-6.50050-8 ◽

2014 ◽

pp. 295-300 ◽

Cited By ~ 2

Author(s):

Lixia Kang ◽

Yongzhong Liu ◽

Yuxing Ren ◽

Yazhe Tang

Keyword(s):

Heat Exchanger ◽

Optimal Design ◽

Gpu Acceleration ◽

Sqp Algorithm ◽

Heat Exchanger Networks

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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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Thermal-economic multiobjective optimization of heat pipe heat exchanger for energy recovery in HVAC applications using genetic algorithm

Thermal Science ◽

10.2298/tsci111024203s ◽

2014 ◽

Vol 18 (suppl.2) ◽

pp. 375-391 ◽

Cited By ~ 4

Author(s):

Sepehr Sanaye ◽

Davood Modarrespoor

Keyword(s):

Genetic Algorithm ◽

Heat Exchanger ◽

Heat Pipe ◽

Operating Cost ◽

Design Parameters ◽

Nsga Ii ◽

Pipe Length ◽

Total Cost ◽

Continuous And Discrete Variables ◽

Pipe Heat

Cost and effectiveness are two important factors of heat pipe heat exchanger (HPHE) design. The total cost includes the investment cost for buying equipment (heat exchanger surface area) and operating cost for energy expenditures (related to fan power). The HPHE was thermally modeled using e-NTU method to estimate the overall heat transfer coefficient for the bank of finned tubes as well as estimating pressure drop. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables was applied to obtain the maximum effectiveness and the minimum total cost as two objective functions. Pipe diameter, pipe length, numbers of pipes per row, number of rows, fin pitch and fin length ratio were considered as six design parameters. The results of optimal designs were a set of multiple optimum solutions, called ?Pareto optimal solutions?. The comparison of the optimum values of total cost and effectiveness, variation of optimum values of design parameters as well as estimating the payback period were also reported for various inlet fresh air volume flow rates.

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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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Optimal Design Parameters of Cab’s Isolation System for Vibratory Roller Using a Multi-Objective Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.875.105 ◽

2018 ◽

Vol 875 ◽

pp. 105-112 ◽

Cited By ~ 4

Author(s):

Van Quynh Le ◽

Khac Tuan Nguyen

Keyword(s):

Genetic Algorithm ◽

Optimal Design ◽

Dynamic Model ◽

Nonlinear Dynamic ◽

Ride Comfort ◽

Design Parameters ◽

Nonlinear Dynamic Model ◽

Nsga Ii ◽

Isolation System ◽

Multi Objective

In order to improve the vibratory roller ride comfort, a multi-objective optimization method based on the improved genetic algorithm NSGA-II is proposed to optimize the design parameters of cab’s isolation system when vehicle operates under the different conditions. To achieve this goal, 3D nonlinear dynamic model of a single drum vibratory roller was developed based on the analysis of the interaction between vibratory roller and soil. The weighted r.m.s acceleration responses of the vertical driver’s seat, pitch and roll angle of the cab are chosen as the objective functions. The optimal design parameters of cab’s isolation system are indentified based on a combination of the vehicle nonlinear dynamic model of Matlab/Simulink and the NSGA - II genetic algorithm method. The results indicate that three objective function values are reduced significantly to improve vehicle ride comfort.

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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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