scholarly journals Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Ziaul Huque ◽  
Ghizlane Zemmouri ◽  
Donald Harby ◽  
Raghava Kommalapati
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Cfd Simulation ◽  
Lift Coefficient ◽  
Response Surfaces ◽  
Least Square ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Pareto Optimal Front

A Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL) and Drag Coefficient (CD) data at a range of 0°to 12°angles of attack (α) and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422) for all the six airfoils were obtained. Realizablek-εturbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.

Improved Memetic NSGA-II Using a Deep Neighborhood Search

2021 ◽  
Vol 12 (4) ◽  
pp. 138-154
Author(s):  
Samir Mahdi ◽  
Brahim Nini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Pareto Front ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Slow Convergence ◽  
Multi Objective

Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

scholarly journals Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

2014 ◽  
Vol 63 (3) ◽  
pp. 367-384 ◽  
Author(s):  
K. Pandiarajan ◽  
C.K. Babulal
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Pareto Optimal ◽  
Contingency Analysis ◽  
Nsga Ii ◽  
Generation Cost ◽  
Fuzzy Ranking ◽  
Network Overload ◽  
Line Overload

Abstract This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front

2017 ◽  
Vol 25 (2) ◽  
pp. 309-349 ◽  
Author(s):  
Rubén Saborido ◽  
Ana B. Ruiz ◽  
Mariano Luque
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Weight Vector ◽  
Reference Points ◽  
Pareto Optimal ◽  
Computational Results ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pareto Optimal Front

In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

scholarly journals A Pareto Optimal Solution for Environmental/Economic Power Dispatch using Multi-objective Genetic Algorithm

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Jawad Talaq
Keyword(s):  
Genetic Algorithm ◽  
Environmental Economic ◽  
Economic Power ◽  
Pareto Optimal ◽  
Fuel Cost ◽  
Trade Off ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Power Dispatch ◽  
Economic Power Dispatch

The aim of this paper is to apply genetic algorithm (GA) to the solution of the environmental economic power dispatch problem. The environmental economic power dispatch is a multi-objective optimization problem. Fuel cost is considered as one of the objectives. The other objective is emissions such as SO2 or NOx or a combination of both. A trade-off relation between fuel cost and emissions can be formed through a pareto optimal front. Valve point opening and prohibited operating zones add non-smoothness and non-convexities to the objective functions. Evolutionary algorithms can efficiently solve such non-smooth and non-convex problems. Solutions need to be diversified and distributed among the whole range of the pareto optimal front. This allows operators to trade-off between fuel cost and emissions in feasible optimal regions. Applying genetic algorithm with diversity enhancement proves its effectiveness. Application of the algorithm on three and six unit systems is demonstrated

scholarly journals TỐI ƯU HÓA TIN CẬY VA ĐẬP CỦA ỐNG ĐA TẾ BÀO HÌNH VUÔNG CHỊU TẢI VA ĐẬP XIÊN

2021 ◽  
Vol 45 (03) ◽  
Author(s):  
TRẦN TRỌNG NHÂN
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Pareto Front ◽  
Nsga Ii ◽  
Knee Point

Tối ưu hóa tin cậy va đập của những ống hình vuông đa đa tế bào trong trường hợp va đập xiên được nghiên cứu trong bài báo này. Đối với các cấu trúc này, các chỉ số tin cậy va đập SEA và PCF được thu thập bằng cách sử dụng HYPERMESH / LS-DYNA. Pareto front thu được bẳng cách kết hợp response surface (RS) và Non-dominated Sorting Genetic Algorithm II (NSGA-II). Một “giải pháp tốt hơn” (hay còn gọi là knee point) được xác định từ Pareto front. Kết quả của nghiên cứu này là cơ sở tham khảo cho việc thiết kế các cấu trúc đa tế bào có khả năng tin cậy va đập tốt hơn.

scholarly journals Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 901 ◽  
Author(s):  
Ali Akbar Ahmadi ◽  
Masoud Arabbeiki ◽  
Hafiz Muhammad Ali ◽  
Marjan Goodarzi ◽  
Mohammad Reza Safaei
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Response Surface ◽  
Channel Width ◽  
Pumping Power ◽  
Nsga Ii ◽  
Square Channel ◽  
Cylinder Diameter ◽  
Input Variables ◽  
Volume Method

Nanofluids in minichannels with various configurations are applied as cooling and heating fluids. Therefore, it is essential to have an optimal design of minichannels. For this purpose, a square channel with a cylinder in the center connected to wavy fins at various concentrations of an Al2O3 nanofluid is simulated using the finite volume method (FVM). Moreover, central composite design (CCD) is used as a method of design of experiment (DOE) to study the effects of three input variables, namely the cylinder diameter, channel width, and fin radius on the convective heat transfer and pumping power. The impacts of the linear term, together with those of the square and interactive on the response variables are determined using Pareto and main effects plots by an ANOVA. The non-dominated sorting genetic algorithm-II (NSGA-II), along with the response surface methodology (RSM) is applied to achieve the optimal configuration and nanofluid concentration. The results indicate that the effect of the channel width and cylinder diameter enhances about 21% and 18% by increasing the concentration from 0% to 5%. On the other hand, the pumping power response is not sensitive to the nanofluid concentration. Besides, the channel width has the highest and lowest effect on the heat transfer coefficient (HTC) and pumping power, respectively. The optimization for a concentration of 3% indicates that in Re = 500 when the geometry is optimized, the HTC enhances by almost 9%, while the pumping power increases by about 18%. In contrast, by increasing the concentration from 1% to 3%, merely an 8% enhancement in HTC is obtained, while the pumping power intensifies around 60%.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

scholarly journals Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Energies ◽  
2013 ◽  
Vol 6 (3) ◽  
pp. 1439-1455 ◽  
Author(s):  
Bogdan Tomoiagă ◽  
Mircea Chindriş ◽  
Andreas Sumper ◽  
Antoni Sudria-Andreu ◽  
Roberto Villafafila-Robles
Keyword(s):  
Genetic Algorithm ◽  
Power Distribution ◽  
Distribution Systems ◽  
Pareto Optimal ◽  
Power Distribution Systems ◽  
Nsga Ii ◽  
Optimal Reconfiguration
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