Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components

2014 ◽  
Author(s):  
Cheng Wang ◽  
Chang-qi Yan ◽  
Jian-jun Wang ◽  
Lei Chen ◽  
Gui-jing Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Nuclear Power ◽  
Optimization Design ◽  
Population Diversity ◽  
Geometric Constraints ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Benchmark Function ◽  
Nonlinear Constraint

Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.

Optimization Design of the Crane Girder Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 123-126
Author(s):  
Peng Fei Wang ◽  
Xiu Hui Diao
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Design ◽  
Optimal Solution ◽  
Population Diversity ◽  
Main Beam ◽  
Gantry Crane ◽  
Adaptive Genetic Algorithm ◽  
Accelerate Convergence

With taking weight of single main beam of gantry crane as objective function, and taking main beam upper & lower cored, diagonal & horizontal bracing, and width & weight as design variable, this essay adopted population diversity adaptive genetic algorithm to optimize its structure and improved program design through MATLAB. This algorithm could accelerate convergence speed, which make much it easier to realize comprehensive optimal solution, since it effectively avoided weakness of basic genetic algorithm, such as partial optimal solution, prematurity and being lack of continuity, etc.

Multi-Objective Optimization Design of Vibration Screener Support Spring Parameters with Vibration Mechanics

2012 ◽  
Vol 252 ◽  
pp. 144-148
Author(s):  
Ling Qin Meng ◽  
Zhi Wei Wang
Keyword(s):  
Optimal Design ◽  
Optimization Design ◽  
Building Materials ◽  
Design Method ◽  
Optimization Theory ◽  
Penalty Function Method ◽  
Multi Objective Optimization ◽  
Vertical Stiffness ◽  
Multi Objective ◽  
Future Design

Vibration screener is an important mechanism which is widely applied to metallurgy, building materials, chemical industry, grain, mine, etc. On the basis of deeply studying work principle and support structure of vibration screener, the paper conducts ZKB Linear vibration screener as study object. According to mass of vibration, vertical stiffness, horizontal stiffness, vertical amplitude, horizontal amplitude, and free high of vibration screener support spring, the paper conducts the lightest weight of support spring and the biggest fatigue safety factor as the objective function of multi-objective optimization design to establish a series of constraints. Then using of the penalty function method of optimization theory , the paper gets the optimal design results of support spring and gives the optimal design dimensions, which provides a reliable design method of support spring for the future design of vibration screener and reduce the blindness of the design .

scholarly journals A Modified Multiobjective Self-Adaptive Differential Evolution Algorithm and Its Application on Optimization Design of the Nuclear Power System

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yunjia Yang ◽  
Shinian Peng ◽  
Li Zhu ◽  
Dan Zhang ◽  
Zhifang Qiu ◽  
...  
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Power System ◽  
Differential Evolution ◽  
Nuclear Power ◽  
Optimization Design ◽  
Differential Evolution Algorithm ◽  
Nuclear Power System ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

Optimization Design of Anti-Sliding Pile Based on Genetic Algorithm

2011 ◽  
Vol 71-78 ◽  
pp. 3914-3917
Author(s):  
Yin Xu ◽  
Qing Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Slope Stability ◽  
Optimization Design ◽  
Fitness Function ◽  
Sliding Surface ◽  
Mutation Probability ◽  
Reinforcement Measures ◽  
Sliding Force

It is important to search the position of the dangerous sliding surface and to design the reinforcement measures in the study of the slope stability. At present, anti-sliding pile is one of the popular reinforcement measures. In this paper, to optimal design the anti-sliding pile, the residual thrust method (RTM) and genetic algorithm are carried out, while the idea that based on the position of the largest anti-sliding force rather than the most dangerous sliding surface to design the anti-sliding piles is presented. The new idea eliminates the possible unsafe situation. In the process of the optimization design of anti-sliding pile, firstly, chromosomes in the genetic algorithm are coded by using decimal method. Secondly, taking the residual thrust as the fitness function in the genetic algorithm, cross probability and mutation probability are all adjusted according to the fitness of individual population, while the best individual population is saved as population of the next step so that to improve the efficiency. Finally, the sliding surface which needs the largest anti-sliding force is searched out. The reasonability and the reliability of the idea are verified by an example.

Optimal Design of Gears by Means of Genetic Algorithm and Neural Network

2013 ◽  
Vol 397-400 ◽  
pp. 816-820
Author(s):  
Yong Gang Li, ◽  
Yong Mei Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Relative Parameter

Optimal design of gears was complicated with much difficulty to determine the parameter of strength constraint equation, and find the optimal solution. Used BP Neural Network to approximate the relative parameter of gears optimization design which was shown by chart. Used Genetic Algorithm to search the optimal solution. The result shows that the application of Genetic Algorithm and Neural Network in gear optimization is effective.

scholarly journals Prediction of Wave-induced Liquefaction using Artificial Neural Network and Wide Genetic Algorithm

2017 ◽  
pp. 1
Author(s):  
Dwi Kristianto ◽  
Chastine Fatichah ◽  
Bilqis Amaliah ◽  
Kriyo Sambodho
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Back Propagation ◽  
Global Optimum ◽  
Population Diversity ◽  
Local Optimum ◽  
Artificial Neural ◽  
Wave Induced

The hassle of analytical and numerical solution for liquefaction modeling, repetitive laboratory testing and expensive field observations, have opened opportunities to develop simple, practical, inexpensive and valid prediction of wave-induced liquefaction. In this study, Artificial Neural Network (ANN) regression modeling is used to predict the depth of liquefaction. Despite of using Back Propagation (BP) to train ANN, a modified Genetic Algorithm (called as Wide GA, WGA) is used as ANN training method to improve ANN prediction accuracy and to overcome BP weaknesses such as premature convergence and local optimum. WGA also aim to avoid conventional GA weaknesses such as low population diversity and narrow search coverage. Key WGA operations are Wide Tournament Selection, Multi-Parent BLX-? Crossover, Aggregate Mate Pool Mutation and Direct Fresh Mutation-Crossover. ANN prediction accuracy measured by Median APE (MdAPE). Global optimum solution of WGA is best ANN connections weights configuration with smallest MdAPE.

Minimizing Stress Concentrations in Laminated Composites by Genetic Algorithm

Materials ◽  
2005 ◽  
Author(s):  
H. K. Cho ◽  
R. E. Rowlands
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Laminated Composite ◽  
Layered Composite ◽  
Local Optimum ◽  
Optimization Approach ◽  
Stress Concentrations ◽  
Design Variables ◽  
As Stress ◽  
Stress Variations

Design optimizations using a genetic algorithm (GA) are well suited for problems having many design variables and local optimum design points. Concomitant with recent manufacturing advances, the concept is utilized here to minimize the tensile stress concentration in a perforated laminated composite by orientating the fibers locally both within the plies and from ply-to-ply. The current optimization approach is advantageously conducted in conjunction with FEA. The geometry is discretized into general 3D solid 20-node isoparametric layered composite elements of our own design. Solid, rather than plate, elements enable one to reliably account for features such as stress variations within and between individual plies. A parallel computing scheme is implemented between the FEA and GA optimization. Design optimization variables are local fiber directions within discrete finite elements and within respective plies of the laminate. Since fiber orientations are optimized locally within individual plies, the technique provides more than just a favorable stacking sequence of various rectilinearly orthotropic plies having different fiber orientations.

scholarly journals A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 984-991 ◽  
Author(s):  
Aimeng Wang ◽  
Jiayu Guo
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Electric Vehicle ◽  
Optimization Design ◽  
Fe Simulation ◽  
Hybrid Genetic Algorithm ◽  
Ipm Machine ◽  
Simulation Results ◽  
Rotor Structure

AbstractA novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Optimization of the transmission characteristics of an HMCVT for a high-powered tractor based on an improved NSGA-II algorithm

2022 ◽  
pp. 095440702110679
Author(s):  
Jiang Li ◽  
Zhiqiang Zhai ◽  
Zhansheng Song ◽  
Shenghui Fu ◽  
Zhongxiang Zhu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Structural Parameters ◽  
Population Diversity ◽  
Operating Conditions ◽  
Power Performance ◽  
Transmission Characteristics ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective

The hydro-mechanical continuously variable transmission (HMCVT) is a critical component of the power transmission system in a tractor. However, the complexity of the operating conditions imposes high requirements on the transmission characteristics. To improve the powerful performance and economy of HMCVTs and satisfy the operational demands of high-powered tractors, a new optimization design method for the characteristic parameters of an HMCVT is proposed. First, the characteristics of an HMCVT are modeled, and the influence of the structural parameters on the transmission characteristics is analyzed. Then, HMCVT performance evaluation indexes are formulated. In accordance with the speed regulation of system, power performance, and economy characteristics, a multi-objective optimization mathematical model is established, and an improved fast non-dominated sorting genetic algorithm (INSGA-II) is designed. The introduction of a normal distribution crossover operator (NDX) and an improved adaptive adjustment mutation operator not only ensures the population diversity but also improves the Pareto solution convergence properties during the process of genetic evolution. The superiority of INSGA-II is verified by comparison with a traditional multi-objective genetic algorithm. Finally, the optimization results show that the torque ratio is increased by approximately 2.81%, 14.32%, 2.31%, and 15.07% in HM1, HM2, HM3, and HM4 respectively. The transmission efficiency is increased by approximately 3.48% and 1.97% in HM1 (HM3) and HM2 (HM4). Also, INSGA-II finds the optimal solution with a faster speed and shorter optimization time than MULGA. This research can serve as a reference for the design and optimization of HMCVTs for high-powered tractors.

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