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

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
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.

Multi-Objective Optimization of the Impingement-Film Cooling Structure of a HPT Endwall Using Conjugate Heat Transfer CFD

2016 ◽  
Author(s):  
Zhongran Chi ◽  
Haiqing Liu ◽  
Shusheng Zang
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Wall Temperature ◽  
Conjugate Heat Transfer ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Lift Off ◽  
Optimization Search

This paper discusses the approach of cooling design optimization of a HPT endwall with 3D Conjugate Heat Transfer (CHT) CFD applied. This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which were different for each cooling cavity. The optimization objectives were to reduce the wall temperature level and also to increase the aerodynamic performance of the gas turbine. The optimization methodology consisted of an in-house parametric design & CFD mesh generation tool, a CHT CFD solver, a database of wall temperature distributions, a metamodel, and a genetic algorithm (GA) for evolutionary multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently predict the aerodynamic loss and the wall temperature distribution of a new individual based on the database, was developed and coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to accelerate the optimization process. Through optimization search, the Pareto front of the problem was found costing only tens of CFD runs. By comparing with additional CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal spacing of each impingement array was decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that using cylindrical film holes near throat could benefit both aerodynamics and cooling.

scholarly journals Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes forEucalyptus fastigata

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Oliver Chikumbo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Fitness Sharing ◽  
Pareto Ranking ◽  
Ranking Scheme ◽  
Volume Production ◽  
Nondominated Sorting

A stand-level, multiobjective evolutionary algorithm (MOEA) for determining a set of efficient thinning regimes satisfying two objectives, that is, value production for sawlog harvesting and volume production for a pulpwood market, was successfully demonstrated for aEucalyptus fastigatatrial in Kaingaroa Forest, New Zealand. The MOEA approximated the set of efficient thinning regimes (with a discontinuous Pareto front) by employing a ranking scheme developed by Fonseca and Fleming (1993), which was a Pareto-based ranking (a.k.a Multiobjective Genetic Algorithm—MOGA). In this paper we solve the same problem using an improved version of a fitness sharing Pareto ranking algorithm (a.k.a Nondominated Sorting Genetic Algorithm—NSGA II) originally developed by Srinivas and Deb (1994) and examine the results. Our findings indicate that NSGA II approximates the entire Pareto front whereas MOGA only determines a subdomain of the Pareto points.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

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.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
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.

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

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 260 ◽  
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.

scholarly journals A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiwang Guo ◽  
Shixin Liu
Keyword(s):  
Multiobjective Optimization ◽  
Scatter Search ◽  
Nsga Ii ◽  
Disassembly Sequence ◽  
Selective Disassembly ◽  
Local Search Strategy ◽  
Search Approach ◽  
Nondominated Sorting ◽  
Disassembly Time

Disassembly sequence has received much attention in recent years. This work proposes a multiobjective optimization of model for selective disassembly sequences and maximizing disassembly profit and minimizing disassembly time. An improved scatter search (ISS) is adapted to solve proposed multiobjective optimization model, which embodies diversification generation of initial solutions, crossover combination operator, the local search strategy to improve the quality of new solutions, and reference set update method. To analyze the effect on the performance of ISS, simulation experiments are conducted on different products. The validity of ISS is verified by comparing the optimization effects of ISS and nondominated sorting genetic algorithm (NSGA-II).

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