scholarly journals Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Self-Pressurized Drip Irrigation Network System Based on the Genetic Algorithm

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 489 ◽  
Author(s):  
Rong-Heng Zhao ◽  
Wu-Quan He ◽  
Zong-Ke Lou ◽  
Wei-Bo Nie ◽  
Xiao-Yi Ma
Keyword(s):  
Genetic Algorithm ◽  
Drip Irrigation ◽  
Optimization Design ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Network System ◽  
Pipe Network ◽  
Network Systems ◽  
Irrigation Network ◽  
Effective Performance

A synchronous optimization method for self-pressure drip irrigation pipe network system is proposed. We have generalized the optimization design problem of the system and have established the mathematical models for the simultaneous optimization design of pipeline layout and pipe diameters. A genetic algorithm based on the infeasibility degree of the solution was used to solve the model. A typical example is used to validate the presented method. The method exhibits effective performance in the case studied. Designers can use the results of this study to efficiently design self-pressurized drip irrigation network systems.

Dynamics Design of a Planar Controllable Five Bar Mechanism Based on Genetic Algorithm

2007 ◽  
Author(s):  
Ke Zhang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Method ◽  
Parallel Robot ◽  
Bond Graph ◽  
Dynamics Analysis ◽  
Hybrid Mechanism ◽  
Ga Algorithm ◽  
Power Bond Graph ◽  
Bond Graph Theory

A hybrid five bar mechanism is a typical planar parallel robot. It is a configuration that combines the motions of two characteristically different motors by means of a five bar mechanism to produce programmable output. Hybrid five bar mechanism is the most representative one of hybrid mechanism. In this paper, considering the bond graph can provide a compact and versatile representation for kinematics and dynamics of hybrid mechanism, the dynamics analysis for a hybrid five-bar mechanism based on power bond graph theory is introduced. Then an optimization design of hybrid mechanism is performed with reference to dynamic objective function. By the use of the properties of global search of genetic algorithm (GA), an improved GA algorithm is proposed based on real-code. Optimum dimensions are obtained assuming there are no dimensional tolerances or clearances. Finally, a numerical example is carried out, and the simulation result shows that the optimization method is feasible and satisfactory in the design of hybrid mechanism.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

Optimization Design of High-Performance Concrete Based on Genetic Algorithm Toolbox of Matlab

2011 ◽  
Vol 250-253 ◽  
pp. 2672-2677 ◽  
Author(s):  
Xian Song Xie ◽  
Dong Jin Yan ◽  
Yue Zhai Zheng
Keyword(s):  
Genetic Algorithm ◽  
High Performance ◽  
Optimization Design ◽  
High Performance Concrete ◽  
Economic Cost ◽  
Mathematic Model ◽  
Optimization Method ◽  
Engineering Practice ◽  
Mix Proportion ◽  
Save Energy

Genetic algorithm is a non-numerical optimization method which based on natural selection and population genetics.Using genetic algorithm to optimize the mix proportion design of high performance concrete, it takes into account the economic profitability on the foundation of satisfying the requirements of durability, strength, workability and dimensional stability of concrete, it establishes a mathematic model applying the performance of material as constraint condition, and the economic cost as optimization target.Using binary coding to represent the chromosome bit serial of individual, through selection, crossover, mutation and other genetic operator to conduct global probability search, taking the principle of “survival of the fittest”, finally achieve the best population and individual. Compare the results of optimization with the mix proportion in practice engineering case, we can reach the conclusion that Genetic Algorithm could reduce the cost, save energy, provides better use value on engineering practice.

The Application and Comparison of Two Different Optimization Algorithms in the Optimization Design of Hydrofoil

2012 ◽  
Vol 178-181 ◽  
pp. 2871-2876
Author(s):  
Chao Wang ◽  
Feng Feng ◽  
Xin Chang ◽  
Chun Yu Guo ◽  
Yang Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Optimization Method ◽  
Flow Solver ◽  
Propeller Design ◽  
Section Design ◽  
Annealing Algorithm

Hydrofoil is the important part of ship design and diverse motion equipment. The optimization design of hydrofoil section on lift-to-drag radio with genetic algorithm (GA) and simulated annealing algorithm are demonstrated, and the method on the hydrofoil section design of the propeller design will be done. Objective function and fitness of every individual are provided by flow solver of panel method. The optimization method on design of hydrofoil section on lift-to-drag is successfully used. The optimization results show the combination of optimization algorithm is feasible at the optimal design of hydrofoil sections. What’s more, a comparison between two different optimization algorithms is made, a conclusion that the simulated annealing algorithm is better then the genetic algorithm is obtained.

Optimization Design and Experimental Study of a Two-disk Rotor System Based on Multi-Island Genetic Algorithm

2019 ◽  
Vol 36 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Jingjing Huang ◽  
Longxi Zheng ◽  
Chris K Mechefske ◽  
Bingbing Han
Keyword(s):  
Genetic Algorithm ◽  
Vibration Amplitude ◽  
High Speed ◽  
Optimization Design ◽  
Critical Speed ◽  
Optimization Method ◽  
Rotor System ◽  
Flexible Rotor ◽  
Optimum Position ◽  
Transient Experiments

Abstract Based on rotor dynamics theory, a two-disk flexible rotor system representing an aero-engine with freely supported structure was established with commercial software ANSYS. The physical model of the two-disk rotor system was then integrated to the multidisciplinary design optimization software ISIGHT and the maximum vibration amplitudes experienced by the two disks when crossing the first critical speed were optimized using a multi-island genetic algorithm (MIGA). The optimization objective was to minimize the vibration amplitudes of the two disks when crossing the first critical speed. The position of disk 1 was selected as the optimization variable. The optimum position of disk 1 was obtained at the specified constraint that the variation of the first critical speed could not exceed the range of ±10 %. In order to validate the performance of the optimization design, the proof-of-transient experiments were conducted based on a high-speed flexible two-disk rotor system. Experimental results indicated that the maximum vibration amplitude of disk 1 when crossing the first critical speed declined by 60.9 % and the maximum vibration amplitude of disk 2 fell by 63.48 % after optimization. The optimization method found the optimum rotor positions of the flexible rotor system which resulted in minimum vibration amplitudes.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

The Truss Structural Optimization Design Based on Improved Hybrid Genetic Algorithm

2010 ◽  
Vol 163-167 ◽  
pp. 2304-2308
Author(s):  
Feng Guo Jiang ◽  
Zhen Qing Wang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Structural Optimization ◽  
Optimization Design ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
General Search ◽  
Genetic Arithmetic ◽  
Iteration Analysis

Genetic arithmetic operators in genetic algorithm be improved , and a hybrid genetic algorithm of a gradient algorithm combining with the genetic algorithm be given against to the defects such as premature，slow on convergence rate，weak in the ability of local search ，all these appeared on the progress of genetic algorithm's iteration. Analysis result indicate that not only strong on the local search capacity of gradient algorithm be exhibited but also strong on the general search capacity of genetic algorithm be combined based on the hybrid genetic algorithm ,which make phenomenon of premature avoid, and the rate of convergence be improved greatly. Concrete calculated example indicated that the hybrid genetic algorithm is an effective structural optimization method.

A CAN Topology Optimization of Pure Electric Vechicle

2013 ◽  
Vol 791-793 ◽  
pp. 1819-1823
Author(s):  
Xi Chen ◽  
Jian Wu ◽  
Yang Zhao ◽  
Hong Tao Bai
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Network Topology ◽  
Network Performance ◽  
Can Bus ◽  
Optimization Method ◽  
Network System ◽  
Multi Objective Optimization ◽  
Upper Limit ◽  
Vehicle Network

In the design of CAN network system, as CAN bus topology will affect network performance and cost, it is important to optimize the network topology. This paper analyzes the CAN busload based on CAN protocol, calculates the upper limit of transmission message frames in a single CAN bus. As the amount of information on the bus is increasing, a single CAN bus cannot meet the communication requirements, we put forward dividing the network into multiple homogeneous segments via multi-objective optimization method, developing a genetic algorithm strategy and solving the problem in a MATLAB platform. Finally utilize the method to design a pure electric vehicle network topology.

scholarly journals Synthetical Optimization of a Gravity-Driven Irrigation Pipeline Network System with Pressure-Regulating Facilities

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1112 ◽  
Author(s):  
Rong-Heng Zhao ◽  
Zi-Han Zhang ◽  
Wu-Quan He ◽  
Zong-Ke Lou ◽  
Xiao-Yi Ma
Keyword(s):  
Optimization Design ◽  
Pressure Head ◽  
System Optimization ◽  
Network System ◽  
Pipe Network ◽  
Project Cost ◽  
Main Pipe ◽  
Fixed Proportion ◽  
Gravity Driven ◽  
Elevation Difference

Due to the influence of topographic drops, a large elevation difference often occurs in the middle and lower sections of the main pipe of a gravity-driven irrigation pipe network (GDIPN) system. This elevation difference must be reduced appropriately through pressure reduction facilities (pressure-regulating ponds (PRPs) or pressure-reducing valves (PRVs)). The number and locations of PRPs are crucial factors in regulating and balancing the pressure head of the main pipe of a GDIPN system as well as in reducing the project cost. However, there are few studies on the optimization of this kind of pipe network system. In this paper, first, we generalize such type of GDIPN system, and a simplified mathematical model for such system optimization was established. A genetic algorithm based on a fixed proportion and direct comparison (GA-FPDC) was introduced to solve the model. Two existing projects were tested by the proposed method. The results show that the presented method not only improved the design efficiency and rationality but also greatly decreased the project cost. The presented method is effective and efficient to address optimization design of such GDIPN system problems.

Magnetic Bearing Design Using Genetic Algorithms

1999 ◽  
Author(s):  
Yong Teng ◽  
Susan Carlson-Skalak ◽  
Eric Maslen
Keyword(s):  
Genetic Algorithm ◽  
Solution Space ◽  
Optimization Method ◽  
Time Frame ◽  
Magnetic Bearing ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Dimensional System ◽  
Reasonable Time Frame ◽  
The Relationship

Abstract A magnetic bearing system is a coupled, nonlinear, high-dimensional system. The relationship among the design parameters, design constraints and the optimization goals is not obvious. Solving this type of design problem within a reasonable time frame is a challenge for any optimization method. This research investigated the simultaneous optimization of the magnetic bearing configuration and bearing locations. A multistage genetic algorithm was developed to search through a discrete and non-convex solution space. Because the genetic algorithm can search through a much larger solution space than any engineer can do, innovative designs different from those using traditional methods can be found.

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