Based on Ant Colony Algorithm Simulation of Single Crystal Alloy Finite Element

2012 ◽  
Vol 201-202 ◽  
pp. 549-552
Author(s):  
Shu Zhang ◽  
Lei Meng
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Ant Colony Algorithm ◽  
Fem Analysis ◽  
Control Parameters ◽  
Algorithm Optimization ◽  
Ant System ◽  
Ant Colonies ◽  
Ant Algorithms ◽  
System A

we have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Based on Ant Colony Algorithm Finite Element Analysis of the Alloy

2012 ◽  
Vol 532-533 ◽  
pp. 1468-1471
Author(s):  
Shu Zhang ◽  
Lei Meng
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Combinatorial Optimization ◽  
Ant Colony Algorithm ◽  
Control Parameters ◽  
Ant System ◽  
Element Analysis ◽  
Ant Colonies ◽  
Ant Algorithms ◽  
System A

we have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated.

Based on a New Immune Genetic Finite Element Analysis of the Alloy

2013 ◽  
Vol 299 ◽  
pp. 233-236
Author(s):  
Lei Meng ◽  
Shu Zhang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Immune System ◽  
Combinatorial Optimization ◽  
Distributed Algorithms ◽  
Control Parameters ◽  
Element Analysis ◽  
Ant Colonies ◽  
Treatment Principles ◽  
System A

We have used the metaphor of ant colonies to define “immune system”, a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of immune-cycle, the up to now best performing of the immune algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated.

Based on Immune Algorithm Heat Treatment Analysis of the Alloy

2012 ◽  
Vol 532-533 ◽  
pp. 763-766
Author(s):  
Shu Zhang ◽  
Lei Meng
Keyword(s):  
Heat Treatment ◽  
Solution Treatment ◽  
Present Form ◽  
Air Cooling ◽  
Ant Colonies ◽  
Ant Algorithms ◽  
System A ◽  
Phase Size ◽  
Phase Combination ◽  
Treatment Analysis

We have used the metaphor of ant colonies to define "the immune system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. 4.2% Re alloy 1320 °C solution treatment can significantly reduce the elements in the interdendritic / arm region segregation, after the high temperature air-cooling solution, γ'-phase combination of butterfly present form, after aging at 1100 °C, the size of about 0.45 m to cubic γ'-phase coherent manner mosaic in g matrix, aging temperature increased, γ' phase size too grew up.

Parallel Ant System Based on OpenMP

2013 ◽  
Vol 765-767 ◽  
pp. 658-661
Author(s):  
Yan Zhang ◽  
Hui Ling Wang ◽  
Xu Li ◽  
Yong Hua Zhang ◽  
Hao Wang
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Parallel Programming ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colonies ◽  
Computing Power

To overcome the limitation of precocity and stagnation in classical ant colony algorithm, this article presents a Parallel Ant System Based on OpenMP. The ant colony is divided into three children ant colonies according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm. By Open Multi-Processing parallel programming idea, the parallel and cooperating optimization of children ant colonies was obtained. It organically combines local search and global search, makes full use of computing power of multi-core CPU, and improves the efficiency significantly. Contrastive experiments show that the algorithm has a better capability of global optimization than traditional ant colony algorithm.

Improved Ant Colony Optimization and Application on TSP

2012 ◽  
Vol 182-183 ◽  
pp. 2055-2058
Author(s):  
Zhi Qiang Fu ◽  
Lei An Liu
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Second Phase ◽  
Ant System ◽  
Ant Colonies ◽  
Convergence Process ◽  
Intelligent Optimization Algorithm ◽  
Ant System Algorithm ◽  
Improved Algorithm

Ant Colony Optimization is an intelligent optimization algorithm from the observations of ant colonies foraging behavior. However, ACO usually cost more searching time and get into early stagnation during convergence Process. We design the improved ant colony algorithm using perturbation method to avoid early stagnation, adjusting volatilization coefficient to increase the exploration of tours at first phase and searching speed at second phase, using hortation method to improved searching efficiency. We apply the improved algorithm on traveling salesman problem showing that the improved algorithm finds the best values more quickly and more stability than Max-Min Ant System algorithm.

Based on Artificial Neural Network Simulation of Alloy Finite Element

2013 ◽  
Vol 710 ◽  
pp. 739-742
Author(s):  
Shu Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fem Analysis ◽  
Algorithm Optimization ◽  
Neural Network Simulation ◽  
Artificial Neurons ◽  
Artificial Neural

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

GIS-based genetic algorithm optimization tool for supporting land use and land management restructuring

2007 ◽  
Vol 13 (1s) ◽  
pp. 33-37
Author(s):  
V. Makarenko ◽  
◽  
G. Ruecker ◽  
R. Sommer ◽  
N. Djanibekov ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Management ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization
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