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The Research in Ant Colony Algorithm and Its Applications on Network Touting

Author: SangLei
Tutor: ZhengWanBo；WuYaoZuo
School: Jilin University
Course: Software Engineering
Keywords: Swarm intelligence algorithm Ant Colony Algorithm Multi-point routing
CLC: TP18
Type: Master's thesis
Year: 2010
Downloads: 66
Quote: 1
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Abstract


Swarm intelligence algorithm since appeared in the early 1990s, and quickly became a research focus of domestic and foreign scholars. The corresponding results emerging, scholars meaningful exploration of swarm intelligence algorithm improvement and application areas. In this paper, swarm intelligence algorithms ant colony algorithm (Ant Colony Algorithm, ACA) based study of algorithm parameters affect the results of the effective theory analysis, adaptation of various parameters of the scope and application of environmental and The improved method is applied to multi-point routing problems, combinatorial optimization problems in the network and get some useful results. The ant colony algorithm has two main characteristics and advantages: First, the algorithm itself has implicit parallelism, individual algorithm independent parallel structure solution by pheromone communication and adjust the solution path; algorithm is robust and the positive feedback and information, since the algorithm in the colony, the individual is to be adjusted by the pheromone Solutions pheromone accumulated a larger impact of the solution, due the pheromone accumulated to a certain extent, it will form a positive feedback, so that ant colony behavior consistency, construct the final solution path. However, the shortcomings of the ant colony algorithm, the algorithm runs much embodies: large NP-HARD problem, the search algorithm characteristics, slow convergence; ant colony algorithm using information positive feedback is easy to make the results into a local optimal solution, rather than global optimal solution. Scholars improvements for these two defects, certain results. In this article we focus on two: the first, the second drawback of the ant colony algorithm improvement by adjusting the distribution of the pheromone, the pheromone positive feedback ability to reduce effective control The algorithm premature convergence, good simulation results. In this article we are important parameters in the ant colony algorithm is analyzed and given a reasonable combination, especially debugging through a series of experiments, we find the key parameters of the constraints solution quality and effective parameters and reasonable analysis, certain results, play a very good role in the expansion of applications of the algorithm. Second, the improved algorithm is applied to multi-point routing problems. Multi-point routing problems into shortest path problem, set a different approach to the multi-point routing problems through different situations, and will be improved ant colony algorithm applied to the problem. Simulation results show that our application is effective. At the end of this article, our future work prospects.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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