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.

The Optimization Design of the Cross-Sectional Layout of an Umbilical Based on the Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Xu Yin ◽  
Zhixun Yang ◽  
Dongyan Shi ◽  
Jun Yan ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Strategy ◽  
Cross Sectional ◽  
Great Probability ◽  
The Cross

Abstract The umbilical which consists of hydraulic tubes, electrical cables and optical cables is a key equipment in the subsea production system. Each components perform different physical properties, so different cross-sections will present different geometrical characteristic, carrying capacities, the cost and the ease of manufacture. Therefore, the cross-sectional layout design of the umbilical is a typical multi-objective optimization problem. A mathematical model of the cross-sectional layout considering geometric and mechanical properties is proposed, and the genetic algorithm is introduced to copy with the optimization model in this paper. A steepest descent operator is embedded into the basic genetic algorithm, while the appropriate fitness function and the selection operator are advanced. The optimization strategy of the cross-sectional layout based on the hybrid genetic algorithm is proposed with the fast convergence and the great probability for global optimization. Finally, the cross-section of an umbilical case is performed to obtain the optimal the cross-sectional layout. The geometric and mechanical performance of results are compared with the initial design, which verify the feasibility of the proposed algorithm.

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.

scholarly journals Optimization design based on hierarchic genetic algorithm and particles swarm algorithm

2018 ◽  
Vol 12 (3) ◽  
pp. 217-222 ◽  
Author(s):  
Lin DengWei
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Fitness Function ◽  
Particle Swarm ◽  
Trust Model ◽  
Particle Swarm Algorithm ◽  
Time Model ◽  
Similarity Algorithm ◽  
Swarm Algorithm ◽  
Hierarchical Genetic Algorithm

For a lot of data, it is time-consuming and unpractical to get the best combination by manual tests. The genetic algorithm can make up for this shortcoming through the optimization of parameters. In this paper, the advantages of traditional similarity algorithm is studied, the time model and the trust model for further filtering are introduced, and the parameters with the combination of hierarchical genetic algorithm and particle swarm algorithm are optimized. In the collaborative filtering algorithm, genetic algorithm is improved with hierarchical algorithm, and the user model and the algorithm process are optimized using the fitness function of selection, crossover, and variation, along with the optimization of recommendation result set. In the algorithm, the global optimal parameters can be calculated with the optimization of the obtained initial data, and the accuracy of the similarity calculation can also be improved. This study does the recommendation and comparison experiment in the MovieLens Dataset, and the results show that, on the basis of obtaining the nearest neighbor user group, the mixing use of the hierarchical genetic algorithm and the particle swarm algorithm can make more improvement in the recommendation quality than that of the traditional similarity algorithm.

Application of Intelligent Algorithm to Transformer Optimal Design

2011 ◽  
Vol 219-220 ◽  
pp. 1578-1583
Author(s):  
Shuang Zhang ◽  
Qing He Hu ◽  
Xing Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Penalty Function ◽  
Fitness Function ◽  
Mutation Rates ◽  
Adaptive Genetic Algorithm ◽  
Speed Of Convergence ◽  
Intelligent Algorithm ◽  
Transformer Model ◽  
Crossover And Mutation

The paper studies transformer optimal design, establishes optimal transformer model based on total owning cost. It adopts penalty function to process objective function with weighted coefficients. For prematurity and low speed of convergence of Simple Genetic Algorithm, improved adaptive genetic algorithm is adopted. It increases crossover and mutation rates, and improves fitness function. It is adopted to search for minimum total owning cost of transformer. The result shows that the algorithm performs well, increases converging speed and betters solution.

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.

A New Flatness Evaluation-Rotation Method Based on GA

2010 ◽  
Vol 139-141 ◽  
pp. 2033-2037 ◽  
Author(s):  
Yan Ming Jiang ◽  
Gui Xiong Liu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Three Dimensions ◽  
Local Optimum ◽  
Mutation Probability ◽  
Projection Length ◽  
Rotation Method ◽  
Minimum Zone ◽  
Crossover And Mutation

Flatness is one fundamental element of geometric forms, and the flatness evaluation is particularly important for ensuring the quality of industrial products. This paper presents a new flatness evaluation in the view of the minimum zone evaluation - rotation method based on genetic algorithm. This method determines the minimum zone through rotating measurement points in three dimensions coordinate. The points are firstly rotated about coordinate axes. Then they are projected in one axis, and the smallest projection length is the flatness value. The rotation angles are optimized by genetic algorithm to improve search efficiency. An exponential fitness function and the rotation angles range is designed on the basis of flatness characteristics. An adaptive mode of crossover and mutation probability is used to avoid local optimum. The results show this method can search the minimum zone and converge rapidly.

Optimal design of flywheels using an injection island genetic algorithm

1999 ◽  
Vol 13 (5) ◽  
pp. 327-340 ◽  
Author(s):  
DAVID EBY ◽  
R.C. AVERILL ◽  
WILLIAM F. PUNCH ◽  
ERIK D. GOODMAN
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
High Speed ◽  
Design Space ◽  
Fitness Function ◽  
Unit Weight ◽  
Computational Time ◽  
Finite Element Code ◽  
Symmetric Plane

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.

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.

scholarly journals Optimal design of watt six-bar transmission mechanism for morphing trailing edge

2021 ◽  
Vol 268 ◽  
pp. 01077
Author(s):  
Jin Zhou ◽  
Shiwei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Optimization Design ◽  
Aerodynamic Performance ◽  
Optimal Shape ◽  
Transmission Mechanism ◽  
Trailing Edge ◽  
Aerodynamic Shape ◽  
Optimization Stage ◽  
Smooth Deformation

The morphing trailing edge could realize a continuous smooth deformation compared with conventional trailing edge, which effectively improves the aerodynamic performance. In this paper, a multi-step optimization design of watt six-bar transmission mechanism for morphing trailing edge is proposed. In the first optimization stage, the most effective aerodynamic shape and bar position in the middle of the morphing trailing edge is determined. In the second optimization stage, a watt six link transmission mechanism is proposed by using genetic algorithm to match the optimal shape from the first optimization stage. Result shows that the optimal design could achieve the determined aerodynamic shape in the first optimization stage perfectly.

Sign in / Sign up

Export Citation Format

Share Document