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Ant Colony groups of intelligent network test platform visualization developed

Author: ShaoXiaoLu
Tutor: DaiWenZhan
School: Zhejiang University of Technology
Course: Control Theory and Control Engineering
Keywords: Swarm Intelligence TSP Ant Colony Algorithm ACO Greedy algorithm JAVA
CLC: TP301.6
Type: Master's thesis
Year: 2010
Downloads: 38
Quote: 0
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Abstract


Swarm Intelligence (SWARM INTELLIGENCE) is the proposed mechanism inspired by bionics, a class of algorithms for solving parallel distributed problem and complex optimization combinatorial problems. Since this type of algorithm has distributed computing, the advantages of positive feedback, robustness and parallelism in computer simulation, pattern recognition, data mining, network communications and many other fields have a wide range of applications. Ant colony algorithms swarm intelligence, at this stage a more in-depth study of a highly efficient optimization algorithm. It is based on the ant colony in foraging reflects the highly intelligent, ant colony as a whole to establish a mutual communication and coordination between the mathematical model and the mathematical model is successfully applied to the traveling salesman problem solving process to obtain the satisfactory optimal solution. In this paper, the principle of swarm intelligence of ant colony algorithm and its basic mathematical model explores and analyzes some limitations exist in the practical application of the ant colony algorithm, based on research, also refer to other different types of The intelligent algorithm of the advantages of traditional ant colony algorithm theory effective improvements. In order to further expand the audience of the ant colony algorithm, this paper also established a web-based ant colony algorithm based test platform. Specific major completed the following work: 1. Too ideal mathematical model to solve the traditional ant colony algorithm depends, there are some differences with the actual situation encountered by the application in the production and living, this paper individuals in the class of ant colony incomplete control may occur at any time to stop or stagnation in the transport and transfer process, under the premise of ensuring the safety of the entire transportation system, a dual constraints ant colony algorithm. This dual constraints ant colony algorithm introduced in the entire transportation network concept of Network Virtual Open, corrected by modifying the distance between the two types of different types of non-reachable node in the network connection between the network nodes effective to solve such problems and get in full compliance with the requirements of the security system is the optimal solution. Access to the high quality of the optimal solution for the ant colony algorithm easy convergence and poor stability, this paper introduces the idea of ??the greedy algorithm in the application of the ant colony algorithm. According to the the local optimum ideological greedy algorithm, this paper presents an ant colony algorithm, the minimum distance equalizer coefficients derived the optimal control strategy as a reverse of the inhibiting factors additional to negative feedback equalization coefficients based on the minimum distance ant colony algorithm solving process, thus improving the hybrid ant colony algorithm improved algorithm can obtain the optimal solution quality. In order to improve the entire algorithm is too strong due to the characteristics of local optimum minimum distance algorithm equalizer coefficients can obtain the optimal solution to the poor quality, estimated to determine the ideological distance between nodes, expanding the minimum distance equalization coefficient arithmetic operation of the process in each step can use to the size of the global information, thereby thus proposed an improved minimum distance algorithm equalizer coefficients, to enhance the minimum distance equalization coefficient algorithm in obtaining optimal solution process in the overall performance. 4 built the basic experimental environment based the INTERNET visualization ant colony algorithm visualization. The experiment is directly embedded in Web pages available to end users, and thus do not need to be downloaded to the local installation. Users can support JAVA plugin by any browser on any operating system to access the platform to the ant colony algorithm and test data related to qualitative research and quantitative analysis. The experimental environment for the user on the platform to develop and validate the new algorithm provides a possibility.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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