scholarly journals Load Distribution Design Pattern for Genetic Algorithm Based Autonomic Systems

2012 ◽  
Vol 38 ◽  
pp. 1905-1915 ◽  
Author(s):  
Vishnuvardhan Mannava ◽  
T. Ramesh
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Design Pattern ◽  
Autonomic Systems

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

Solving Economical Operation Problem of Basin Cascade Plants Based on Bi-Level Coupling Genetic Algorithm

2013 ◽  
Vol 357-360 ◽  
pp. 2921-2924 ◽  
Author(s):  
Xiao Lu Yin ◽  
Yang Yang
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Optimal Operation ◽  
Optimal Decision ◽  
System Management ◽  
Improved Genetic Algorithm ◽  
Hydropower Plants ◽  
Hydropower System ◽  
Upper Level ◽  
Coupling Algorithm

Bi-level coupling algorithm which is based on the improved genetic algorithm was proposed in this paper. Its upper-level module was designed to solve the load optimal distributing problem among the cascade hydropower plants and its lower-level module was designed to solve the generator group optimal-uniting problem among the generators in a hydropower system. This algorithm has advantages in getting the optimal operation schemes of the load distribution, as well as the generator group unitization at the same time. By using a practical basin cascade plants as an example, the results show that the software can carry on rational and optimal decision to the producing operation schemes. The Bi-level coupling algorithm has higher practical value in hydropower system management.

scholarly journals Investigation and Optimization of Load Distribution for Tandem Cold Steel Strip Rolling Process

Metals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 677 ◽  
Author(s):  
Xin Jin ◽  
Changsheng Li ◽  
Yu Wang ◽  
Xiaogang Li ◽  
Yongguang Xiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cold Rolling ◽  
Prediction Model ◽  
Load Distribution ◽  
Rolling Mill ◽  
Fitness Function ◽  
Steel Strip ◽  
Rolling Process ◽  
Cold Rolling Mill ◽  
Tandem Cold Rolling

In order to improve the cold rolled steel strip flatness, the load distribution of the tandem cold rolling process is subject to investigation and optimization. The strip deformation resistance model is corrected by an artificial neural network that is trained with the actual measured data of 4500 strip coils. Based on the model, a flatness prediction model of strip steel is established in a five-stand tandem cold rolling mill, and the precision of the flatness prediction model is verified by rolling experiment data. To analyze the effect of load distribution on flatness, the flatness of stand 4 is calculated to be 7.4 IU, 10.6 IU, and 16.8 IU under three typical load distribution modes. A genetic algorithm based on the excellent flatness is proposed to optimize the load distribution further. In the genetic algorithm, the classification of flatness of stand 4 calculated by the developed flatness prediction model is taken as the fitness function, with the optimal reduction of 28.6%, 34.6%, 27.3%, and 18.6% proposed for stands 1, 2, 3, and 4, respectively. The optimal solution is applied to a 1740 mm tandem cold rolling mill, which reduce the flatness classification from 10.8 IU to 3.2 IU for a 1-mm thick steel strip.

scholarly journals Optimal Segment Control of Active Twist Rotor for Power Reduction in Forward Flight

2021 ◽  
Vol 11 (3) ◽  
pp. 1041
Author(s):  
Xiaochi Zhang ◽  
Zhiqiang Wan ◽  
De Yan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Numerical Model ◽  
Load Distribution ◽  
Power Reduction ◽  
System Complexity ◽  
Optimization Framework ◽  
Steady Level ◽  
Rotor Disk ◽  
Active Twist

The segment control of active twist rotor is investigated to evaluate the effectiveness in rotor power reduction. A numerical model for predicting the isolated rotor power and loads in steady level flights is deployed and validated. A parametric sweep of the amplitude and phase angle for uniform single-harmonic active twist control is conducted to demonstrate the mechanism of active twist control in rotor power reduction. The optimal control schedules and segment layouts of the segment twist control for power reduction while considering saturation limits are obtained using an optimization framework based on genetic algorithm. Up to 5-seg configuration is considered. The results indicate that the segment twist control reduces the rotor power more than the uniform twist control by applying divergent control schedules to each segment. The load distribution of the rotor disk is harmonized in both circumferential and spanwise directions. The 2-seg and 3-seg control configurations are appropriate, while the configurations with more segments yield limited benefits and they may be penalized with an increase in system complexity.

scholarly journals Detecting Meta-Patterns from Frameworks Using Hybrid Genetic Algorithm

2018 ◽  
Vol 7 (2.20) ◽  
pp. 350
Author(s):  
Tapan Kant ◽  
Manjari Gupta ◽  
Anil Kumar Tripathi ◽  
Meeta Prakash
Keyword(s):  
Genetic Algorithm ◽  
Hot Spots ◽  
Design Pattern ◽  
Hybrid Genetic Algorithm ◽  
Object Oriented ◽  
Accurate Identification ◽  
Meta Level ◽  
Process Documentation ◽  
Genetic Algorithm Approach ◽  
Uml Class Diagram

Meta-patterns are a sort of basic object-oriented constructs that have been used to design an object-oriented framework. It has been used to precisely describe possible design pattern of a framework at meta-level to manifest framework hot-spots and its corresponding adaptability. The present study is an attempt to develop a genetic algorithm approach for detecting the types and numbers of meta-patterns. For this purpose we have converted the UML class diagram of object-oriented framework and meta-patterns into directed graph and applied hybrid genetic algorithm. The obtained results from the proposed algorithm are further validated manually with the recall and precision percentage of 86.20 and 80.64 respectively. Overall the study demonstrates the utility of the uniquely proposed algorithm for the near accurate identification of meta-patterns for high reusability. This can be applied on frameworks for assessing the evolution process, documentation of hot-spots and reducing the customization effort. 

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