next step is no longer in accordance with the pervious formula, but set a guiding formula to transfer the state. In the way of pheromone update, the paper is no longer to local update the pheromone on the path after pleted a step,but introducing a feedback factor, through the feedback message by this feedback factor, avoiding the time which the ants go back. And when the pleted the searching of a cycle, it is also no longer to global pheromone update the solution,but record the best path and the worst path, update the pheromone on the best path, and reduct the worst paths' pheromone, while guide the ants search better.
3. This algorithm is applied to wireless ad work QoS multicast routing problem solving, pared with the basic ic ant colony algorithm(GAAC), we can know that the algorithm can enhance the search routing efficiency, and significantly improving the performance of all aspects of the algorithm.
Key Words:Wireless ad works; multicast; QoS constrains; Evolutionary algorithm; ant colony algorithm
目录
摘要................................................................................................................................................. I
Abstract .......................................................................................................................................... III
第一章 绪论................................................................
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