Study of Supervised Segmentation Algorithm Based on Ant Colony for Putamen Region in Brain MRI

2011 ◽  
Vol 340 ◽  
pp. 357-362
Author(s):  
Xiao Jun Liu ◽  
En Qing Dong ◽  
Cheng Lin Lv ◽  
Xiao Yang Li ◽  
Bo Cui ◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Brain Mri ◽  
Solution Process ◽  
Search Space ◽  
Segmentation Algorithm ◽  
Ant Colony ◽  
Object Function ◽  
Snake Model ◽  
General Hypothesis ◽  
Supervised Segmentation

A supervised segmentation algorithm based on ant colony for putamen region in brain MRI is proposed. Since the variance of the putamen template and the searching contour is adopted as the object function, the solution process for the supervised ant colony algorithm model proposed is transformed as the process of the minimum of the object function, or as the optimal searching path problem in the search space. A new scheme for finding search space is proposed, and discusses how to decide the optimal searching scheme. By a general hypothesis for the template, the solution process for the problem is described in detail. A great deal of experimental results show that the supervised segmentation algorithm based on ant colony proposed is better than the Fuzzy c-Mean segmentation, region growth segmentation, GVF(Gradient Vector Flow) Snake model segmentation and the basic ant colony segmentation in the shape of the real template, the shape comparability between adjoining slices and the continuity in single slice. Moreover, the convergence speed of the proposed algorithm is the fastest than the others.

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

Study on Fast Codeword Search Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3768-3773
Author(s):  
Da Yong Zou ◽  
Wei Wu
Keyword(s):  
Vector Quantization ◽  
Ant Colony Algorithm ◽  
Behavior Pattern ◽  
Optimal Path ◽  
Search Time ◽  
Search Space ◽  
Ant Colony ◽  
Computation Complexity ◽  
Setting Parameters ◽  
Quick Search

Vector quantization technology is an efficient and competitive method for data compression, but it is not easy to be implemented because of the comparatively high computation complexity it requires during the coding and decoding process. This paper presents a method of Dual Population Ant Colony Algorithm Codeword Quick Search (DPACAS), exploiting the mechanism of ant trace the optimal path through the pheromones remained, and the behavior pattern of making objects together by picking up and putting down them. It uses Parallel Ant Colony algorithm to sufficiently accelerate the convergence of the ant colony. When the scale of the codebook becomes larger, by setting parameters reasonably and exchanging the pheromones between two species, it broadens the search space, reduces the search time and improves the algorithmic global convergence effectively.

Mass Customization Collaborative Logistics Chain Optimization Based on Improved Mixed Genetic-Ant Colony Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1264-1271
Author(s):  
Wei Ning Tang
Keyword(s):  
Mass Customization ◽  
Ant Colony Algorithm ◽  
Reference Model ◽  
Solution Process ◽  
Solution Algorithm ◽  
Ant Colony ◽  
Optimal Model ◽  
Quality Cost ◽  
Collaborative Logistics ◽  
Evaluation Index Systems

Under condition of mass customization collaborative logistics chain for optimized configuration, taking quality, cost, time and collaboration degree as evaluation index systems, and aggregative value minimum of evaluation indices as object, an optimal model of mass customization collaborative logistics chain was established firstly. Secondly, based on genetic algorithm and ant colony algorithm, an improved mixed genetic-ant colony algorithm was proposed, which was suitable to solve the problem, and the solution process was explained. Finally, an example and comparison were presented to prove the feasibility and validity of the proposed algorithm. The method provides reference model and solution algorithm for mass customization collaboration logistics chain optimization.

Training Algorithm of BP Networks Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 310-313
Author(s):  
Yu Yan Ren ◽  
Ming Sun ◽  
Jie Bao ◽  
Hong Rui Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Extreme Values ◽  
Search Space ◽  
Global Optimum ◽  
Ant Colony ◽  
Learning Ability ◽  
Bp Networks ◽  
Previous Iteration ◽  
Improved Ant Colony Algorithm ◽  
Local Extreme

This study proposes a new method to search optimal parameters of BP networks based on improved ant colony algorithm. The algorithm proposed that each ant searches only around the best solution of the previous iteration with, which can reduce search space fast. is proposed for improving the solution performance to reach global optimum fairly quickly. Simulation results indicate that optimize parameters of BP networks with this method can not only overcome the limitations both the slow convergence and the local extreme values by basic BP algorithm, but also improve the learning ability and generalization ability.

An Ant-Colony Routing Algorithm for Wireless Sensor Network

2013 ◽  
Vol 462-463 ◽  
pp. 112-117 ◽  
Author(s):  
Guang Cai Cui ◽  
Shan Shan Wang ◽  
Jing Jing Fang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Search Space ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Destination Node

According to real-time and limited energy of the wireless sensor network (WSN), this paper proposed an ant-colony algorithm (ACO) for optimal routing. The algorithm limited the search space to next node based on search angle and designed directional pheromones to guide ants to the destination node. Using negative feedback mechanism encouraged later ants to choose the optimal path. When ants are timeout with limited life cycle, go back along the way and reduce the pheromone. Probability-transfer function contained the factors of distance, energy, pheromones and search angle. Compared with other ACOs, the results show that it can balance the energy consumption and improve the routing in aspects of energy, dead nodes, short path and time delay.

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