Calibration of Pipe Friction Factor Based on Improved Genetic Algorithm

2011 ◽  
Vol 217-218 ◽  
pp. 1036-1039 ◽  
Author(s):  
Rui Fen Zhou ◽  
Yu Xue Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Friction Factor ◽  
Water Injection ◽  
Improved Genetic Algorithm ◽  
Oilfield Water

This paper presents a method to calibrate pipe friction factor in oilfield water injection pipeline based on genetic algorithm. For the shortcoming, of basic genetic algorithms, genetic algorithm is made the corresponding improvements, this algorithm is improved global searching capability. Finally, a practical example verified the feasibility of the presented method.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

Research of Chip Placement and Routing Algorithms for Board-Level Photoelectric Interconnection

2014 ◽  
Vol 556-562 ◽  
pp. 1577-1579
Author(s):  
Jian Liu ◽  
Zhao Hua Wu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Routing Algorithms ◽  
Optimal Placement ◽  
Improved Method ◽  
Improved Genetic Algorithm ◽  
C Language ◽  
Placement And Routing ◽  
Board Level ◽  
Improved Algorithm

This document improved genetic algorithm and Intelligent discern points algorithmin chip placement and routing for Board-level photoelectric interconnection, by comparisonofthe algorithm results to verify the effectiveness and practicality of the improved algorithm. First introduced the features of chip placement and routing for Board-level Photoelectric Interconnection. Then describes the improved method of genetic algorithms and intelligent discern points algorithms. Finally, implement algorithm by C language on VC6.0++ platform, while the data import MATLAB to displays the optimal placement and routing results. The results show that the effectiveness of improved algorithm, which has a guiding significance for the chip placement and routing.

Friction Factors of Oilfield Water Injection Network - Research on Solving Approach

2013 ◽  
Vol 765-767 ◽  
pp. 920-923
Author(s):  
Song Wang ◽  
Dong Ma ◽  
Zhong Chen Yu ◽  
Xue Jiao Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Boundary Conditions ◽  
Experimental Method ◽  
Friction Factor ◽  
Flow Rate ◽  
Water Injection ◽  
Optimal Result ◽  
Pipe Section ◽  
Friction Factors ◽  
Oilfield Water

Oilfield water injection network is a special network, where all pressure and flow rate of node is known. Using empirical friction factor value formerly leads to great deviation between optimal result of network and fact operating condition, because of small pipe diameter, severe scale and lots of valves, which is difficult to guide Oilfield production. Firstly, possible pipe combination, that needs to give friction factor value, is found by orthogonal experimental method. Then friction factor sensitivity of pipe section is analyzed in the possible pipe combination by sensitivity analysis, supplementary boundary conditions is gotten, friction factor value of every pipe is given according to solving analogical node equation method.

Friction Factors of Oilfield Water Injection Network - Calculating Cases Analysis

2013 ◽  
Vol 765-767 ◽  
pp. 908-911 ◽  
Author(s):  
Zhong Chen Yu ◽  
Dong Ma ◽  
Song Wang ◽  
Xue Jiao Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Convergence Rate ◽  
Friction Factor ◽  
Water Injection ◽  
Optimal Analysis ◽  
Pipe Section ◽  
Friction Factors ◽  
Engineering Significance ◽  
Oilfield Water

Firstly, possible pipe combination, that needs to give friction factor value, is found by orthogonal experimental method. Then friction factor sensitivity of pipe section is analyzed in the possible pipe combination by sensitivity analysis, supplementary boundary conditions is gotten, friction factor value of every pipe is given according to solving analogical node equation method. At last, Case study showed that the method is true and better in result matching and convergence rate aspect. The approach is a theoretical guarantee to network design and optimal analysis of oilfield water injection network, and has a specified engineering significance.

scholarly journals Dynamic Route Guidance Using Improved Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhanke Yu ◽  
Mingfang Ni ◽  
Zeyan Wang ◽  
Yanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Steady State ◽  
Fitness Function ◽  
Population Diversity ◽  
Route Guidance ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
Guidance Algorithm ◽  
Dynamic Route

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

scholarly journals OPTIMIZATION OF ARCHITECTURAL LAYOUT BY THE IMPROVED GENETIC ALGORITHM

2005 ◽  
Vol 11 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Romualdas Baušys ◽  
Ina Pankrašovaite
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Conceptual Design ◽  
Search Method ◽  
Evolutionary Search ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Current Stage

In this paper we consider architectural layout problem that seeks to determine the layout of Units based on lighting, heating, available sizes and other objectives and constraints. For a conceptual design of architectural layout we present an approach based on evolutionary search method known as the genetic algorithms (GAs). However, the rate of convergence of GAs is often not good enough at their current stage. For this reason, the improved genetic algorithm is proposed. We have analysed and compared the performance of standard and improved genetic algorithm for architectural layout problem solutions and presented the results of performance.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

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