Image Segmentation on Colonies Images by A Combined Algorithm of Simulated Annealing and Genetic Algorithm

2007 ◽  
Author(s):  
Wang Luo ◽  
Weixing Wang ◽  
Haijun Liao
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Combined Algorithm

scholarly journals A Genetic and Simulated Annealing Combined Algorithm for Optimization of Wideband Antenna Matching Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Aixin Chen ◽  
Tiehua Jiang ◽  
Zhizhang Chen ◽  
Yanjun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Power Gain ◽  
Matching Network ◽  
Wideband Antenna ◽  
Matching Networks ◽  
Effectiveness And Efficiency ◽  
Broadband Matching ◽  
Combined Algorithm

A genetic and simulated annealing combined algorithm is presented and applied to optimize broadband matching networks for antennas. As a result, advantages of both the genetic algorithm (GA) and simulated annealing (SA) are taken. Effectiveness and efficiency of the presented combined algorithm are demonstrated by optimization of a wideband matching network for a VHF/UHF discone-based antenna. The optimized parameters provide significant improvements of VSWR and transducer power gain for the antenna.

GMDH Network Model Based on Simulated Annealing and Genetic Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1370-1373
Author(s):  
Xiao Ling Zhang ◽  
Li Kun Zou
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Network Model ◽  
Simulated Annealing Algorithm ◽  
Least Square Method ◽  
Least Square ◽  
Model Based ◽  
Simulated Annealing Genetic Algorithm ◽  
Global And Local ◽  
Combined Algorithm

According to the traditional UMDH network modeling with the least square method to recognize parameters ,it's easy to fall into local minimum ,and with the result that the prediction effect is not ideal. This paper puts forward to combine the simulated annealing algorithm and genetic algorithm, and introduces the combined algorithm to the UMDH network which is used to identify some of its description type coefficient. In this paper ,it describes the simulated annealing genetic algorithm ,and constructs the UMDH network model based on this algorithm, and the model is applied to the simulation of debris flow prediction research ,forecast average relative error reached 3. 54%. The results show that the algorithm not only ensuring the global optimization but also preventing premature convergence, improve the UMDH network model of global and local searching optimal ability further.

Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation

2014 ◽  
Vol 1022 ◽  
pp. 269-272
Author(s):  
Ling Li Zhu ◽  
Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Clustering Analysis ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Initial Condition ◽  
Ct Image ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging, and it can effectively avoid appearing precocity and plunging into local optimum. The example shows that the method is feasible, and better segmentation results have got to satisfy the request for 3D reconstruction, compared with k-means image segmentation and genetic algorithm based image segmentation.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

Parallel recombinative simulated annealing: A genetic algorithm

1995 ◽  
Vol 21 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Samir W. Mahfoud ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing
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