Papers by Keyword: Ant Colony Algorithm (ACA)

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Authors: Wei Wei, Wei Lin, Liang Liu, Zhong Qin Hu
Abstract: Object: To optimize the rigidity registration algorithm between X-ray fluoroscopy and CT, and improve the accuracy of registration. Method: By changing the transmission parameters of the ray tracing, it can obtain the original DRR images and the float DRR image for registration. In trials, it uses ant colony algorithm as the optimized search strategy and Mutual information as the similarity measure. Result: ant colony algorithm and the improved ant colony algorithm compared to the classic Powell algorithm to improve the accuracy of registration about 10% and 20%, achieved good results. Conclusion: Ant Colony Algorithm as optimization search strategy can effectively solve the local minima problem in 2D-3D medical image registration, and further improve the accuracy of registration.
Authors: Pu Wang, Bo Liu, Li Jia Zhang, Yong Jun Wang, Qing Hua Tian, Xiao Li Yin, Xiang Jun Xin
Abstract: Dynamic RWA algorithm which supports differentiating business service quality levels becomes a hot research in optical networks. This paper proposes a better ant colony algorithm for routing and wavelength assignment in ASON with supporting differentiating SLA of services and 10P bps network capacity. Firstly, the algorithm adds the concept of business interval period M to dynamically adjust the wavelength grouped in different periods So as to better meet the sercice requests of different levels; Secondly the algorithm adds a link selection control factor in the transition probability function ACA in order to achieve a better link load balancing. Simulation results show that after joining the cyclical and link selection control factor, blocking rate becomes lower and the load of network becomes more evenly.
Authors: Xin Wu Li
Abstract: Color management for liquid crystal display is one of the key techniques in the color image reproduction. A new color management model is presented based on overcoming flaws and limitations of current ways of liquid crystal display color management . First, the paper takes standard color target for experimental sample, and substitutes color blocks in color shade district for complete color space. Second, data collecting method is introduced and some data bases for deducing the model are created. Then, ant colony algorithm is corrected to speed up model’s convergence and a new model for liquid crystal display color management based on improved ant colony algorithm is deduced and analyzed. Finally the experimental results show that the model can improve color management accuracy of liquid crystal display and can be used in its color management practically.
Authors: Yu Zhong Liu, Hua Ping Yu
Abstract: Aimed at solving premature convergence and low speed in heuristic algorithms for TSP problems, this paper analyzed the principle of Max-Min Ant colony algorithm (MMAS) and Lin-Kernighan algorithm, then proposed a dynamic exchange of Max-Min Ant colony algorithm (MMAS-LK). The new algorithm used MMAS to initially a set of the solutions in the early state, then utilized the improved Lin-Kernighan algorithm for local optimization, and dynamic adjustment parameters according to the process of computing avoid falling into local optimum. The simulation results showed that the proposed algorithm compared with the original MMAS and Lin-Kernighan algorithm, it has a better speed and precision in the TSP problem.
Authors: Yuan Bin Hou, Yang Meng, Jin Bo Mao
Abstract: According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.
Authors: Xiaoqin Zhang, Guo Jun Jia
Abstract: Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.
Authors: Zhi Ping Hou, Feng Jin, Qin Jian Yuan, Yong Yi Li
Abstract: Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem. A new type of bionic algorithm-ant colony algorithm is very appropriate to solve Vehicle Routing Problem because of its positive feedback, robustness, parallel computing and collaboration features. In view of the taxi route optimization problem, this article raised the issue of the control of the taxi, by using the Geographic Information System (GIS), through the establishment of the SMS platform and reasonable taxi dispatch control center, combining ant colony algorithm to find the most nearest no-load taxi from the passenger, and giving the no-load taxi the best path to the passenger. Finally this paper use Ant Colony laboratory to give the simulation. By using this way of control, taxis can avoid the no-load problem effectively, so that the human and material resources can also achieve savings.
Authors: Yan Jun Luo, Zhao Yu Bei
Abstract: Ant colony algorithm has disadvantages such as long researching time and easily relapsing into local optimization. Artificial fish-swarm algorithm is presented to conquer the disadvantages. The combination of the two algorithms is applied in function optimization to overcome the limitation that the ant colony algorithm does not fit to solve continuous space optimization. The tested function shows the effect of the method.
Authors: Jing Hua Zhao, Jie Lin
Abstract: The coordination of multi-enterprise production scheduling with partial information sharing is important in supply chain management. This paper proposes a multi-enterprise scheduling model based on ant colony algorithm. The model managed by agents and the enterprises interact with each other to evaluate the schedules, and build new schedules if any other enterprise is unsatisfied with the status quo. This process will repeat until all enterprises are satisfied with the schedules. Finally, the paper takes an example shows that the algorithm is effective and feasible.
Authors: Yun Hua Guo, De Qian Shi
Abstract: A generalized assignment problem under the special constraints is proposed, which can be solved by the ant colony algorithm (ACA). But the ACA for the problem is easy to fall in local optima because the constraints are relatively complicated. In this paper, the diversity in the results is maintained and the convergent speed is elevated by an adaptive method of updating pheromone and a mutation strategy for clearing up invalid assignment. The computer experiments demonstrate that the optimization performance is improved by the proposed algorithm, which is more suitable for the real-time application.
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