Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

Fuzzy Clustering Segmentation Algorithm Research on Sports Image

2013 ◽  
Vol 339 ◽  
pp. 297-300 ◽  
Author(s):  
Xue Jiao Dong ◽  
Xiao Yan Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Practical Method ◽  
Global Optimal Solution ◽  
Fcm Algorithm ◽  
Fast Convergence Rate ◽  
Fuzzy C Means Clustering ◽  
Global Optimal ◽  
Theory Research

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM is proposed. Firstly, the approximate optimal solution obtained by the improved FCM algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution, the final segmentation result is achieved at last. The experiment results prove that in the view of the flame image segmentation, this method shows the good clustering performance and fast convergence rate, and has the widespread serviceability, so it is the practical method in image segmentation.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

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.

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

Genetic Algorithm Based on Permutation Distance for Laser Antimissile Problem

2010 ◽  
Vol 40-41 ◽  
pp. 488-493
Author(s):  
Yong Sun ◽  
Mao Rui Zhang ◽  
Wei Wei Liu ◽  
Li Na Zhang ◽  
He Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
The Real ◽  
Real Application ◽  
Global Optimal ◽  
Simulation Results

The genetic algorithm based on permutation distance definition is used to solve the laser antimissile system. When faced with multiple attacking targets, it is clearly important for the laser antimissile system to determine the sequence of the attacking targets to be intercepted so that the maximum attacking targets are destroyed. It’s very difficult to find the global optimal solution, especially when the number of the targets is greater than six. The permutation distance definition is introduced to measure the distribution of the population. The successive zeros permutation distance is to stop the genetic algorithm iterations. Finally, taking ten targets as an example, the simulation results show that the convergence of the algorithm is fast and this achievement can be used in the real application.

A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

2012 ◽  
Vol 182-183 ◽  
pp. 1681-1685
Author(s):  
Tian Wu Zhang ◽  
Gong Bing Guo
Keyword(s):  
Local Minimum ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Noise Data ◽  
Global Optimal ◽  
Minimum Points ◽  
Better Than

Fuzzy C-means clustering algorithm(FCM) is sensitive to its initialization of value and noise data and easy to fall into local minimum points, while it can’t get the global optimal solution. This paper introduces gravitation and density weight into the process of clustering, and proposes a gravitational Fuzzy C-Means clustering algorithm based on density weight (DWGFCM). The experimental results show that the algorithm has better global optimal solution, overcomes the shortcomings of traditional Fuzzy C-means clustering algorithm. Clustering results are obviously better than FCM algorithm.

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Chaotic Mutation Bee Evolution Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1636-1639
Author(s):  
Yuan Yao ◽  
Yan Ling Zou ◽  
Qi Man Wu ◽  
Zhong Ren Guan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Fast Convergence ◽  
Evolution Process ◽  
Global Optimal Solution ◽  
Iterative Calculation ◽  
Global Optimal ◽  
Evolution Algorithm ◽  
The Individual

In order to make full use of chaotic mutation genetic algorithm and the chaotic mutation and bee evolution algorithm, the characteristics of the two algorithms, and the combination of chaotic mutation bee evolution algorithm is proposed. The algorithm in bee evolution process, to adapt to the value of group of smaller portions of the variation of individuals to chaos; to adapt to the value of group of large part of the individual, to the best individual as the center, change crossover operation, each generation is the best individual immune evolutionary iterative calculation. Thus, as the iteration, the algorithm not only fast convergence, and can also by a higher accuracy by the global optimal solution.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

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