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.

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

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.

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.

scholarly journals Supplier Selection and Production Planning by Using Guided Genetic Algorithm and Dynamic Nondominated Sorting Genetic Algorithm II Approaches

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
H. S. Wang ◽  
C. H. Tu ◽  
K. H. Chen
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supplier Selection ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Maximum Efficiency ◽  
Nsga Ii ◽  
Defect Rate ◽  
The Mathematical Model ◽  
Nondominated Sorting

Through the global supply chain (SC), numerous firms participate in vertically integrated manufacturing, and industrial collaboration and cooperation is the norm. SC management activities, such as delivery time, quality, and defect rate, are characterized by uncertainty. Based on all of the aforementioned factors, this study established a multiobjective mathematical model, integrating the guided genetic algorithm (Guided-GA) and the nondominated sorting genetic algorithm II (NSGA-II), developed in previous studies, to improve the mechanisms of the algorithms, thereby increasing the efficiency of the model and quality of the solution. The mathematical model was used to address the problems of supplier selection, assembly sequence planning, assembly line balancing, and defect rate, to enable suppliers to respond rapidly to sales orders. The model was empirically tested using a case study, showing that it is suitable for assisting decision makers in planning production and conducting SS according to sales orders, enabling production activities to achieve maximum efficiency and the competitiveness of firms to improve.

Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 856-861
Author(s):  
Yuan Xie
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Due Dates ◽  
Nsga Ii ◽  
Optimal Solution Set ◽  
Parallel Machines Scheduling ◽  
Nondominated Sorting ◽  
Completion Times

A kind of unrelated parallel machines scheduling problem is discussed. The memberships of fuzzy due dates denote the grades of satisfaction with respect to completion times with jobs. Objectives of scheduling are to maximize the minimum grade of satisfaction while makespan is minimized in the meantime. Two kind of genetic algorithms are employed to search optimal solution set of the problem. Both Niched Pareto Genetic Algorithm (NPGA) and Nondominated Sorting Genetic Algorithm (NSGA-II) can find the Pareto optimal solutions. Numerical simulation illustrates that NSGA-II has better results than NPGA.

scholarly journals A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Guiliang Gong ◽  
Qianwang Deng ◽  
Xuran Gong ◽  
Like Zhang ◽  
Haibin Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Two Stage ◽  
Nsga Ii ◽  
Routing Problem ◽  
Optimization Mechanism ◽  
Nondominated Sorting

A new closed-loop supply chain logistics network of vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) dominated by remanufacturer is constructed, in which the customers are originally divided into three types: distributors, recyclers, and suppliers. Furthermore, the fuel consumption is originally added to the optimization objectives of the proposed VRPSPD. In addition, a bee evolutionary algorithm guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) with a two-stage optimization mechanism is originally designed to solve the proposed VRPSPD model with three optimization objectives: minimum fuel consumption, minimum waiting time, and the shortest delivery distance. The proposed BEG-NSGA-II could conquer the disadvantages of traditional nondominated sorting genetic algorithm II (NSGA-II) and algorithms with a two-stage optimization mechanism. Finally, the validity and feasibility of the proposed model and algorithm are verified by simulating an engineering machinery remanufacturing company’s reverse logistics and another three test examples.

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