改进交互式蚁群算法及其应用

发布时间：2018-05-04 13:41

  本文选题：交互式蚁群优化 + 蚁群优化　； 参考：《计算机科学与探索》2016年12期

【摘要】：交互式蚁群优化(interactive ant colony optimization,i ACO)是一种利用人来评价解的优劣而进行系统优化的技术,可以求解性能指标不能或者难以数量化的优化问题。分析了交互式蚁群优化模型面临的研究困难。针对Tanabe等人提出的交互式蚂蚁算法性能不足的问题,提出利用全局历史最优解进行信息素的更新,并将信息素限定在一定区间内的改进交互式蚁群优化算法,从人机交互角度讨论了解的构造方法和人的评价策略。最后,利用函数优化和汽车造型设计进行了实验,运行结果表明算法具有较高优化性能。
[Abstract]:Interactive ant colony optimization (ACO) is a system optimization technique using human to evaluate the solution, which can solve the optimization problem which can not be quantified or can not be quantified. The research difficulties of interactive ant colony optimization model are analyzed. In order to solve the problem of poor performance of interactive ant algorithm proposed by Tanabe et al, an improved interactive ant colony optimization algorithm, which uses global historical optimal solution to update pheromone and limits pheromone to a certain interval, is proposed. From the point of view of human-computer interaction, the construction method of understanding and the evaluation strategy of human are discussed. Finally, the experiments are carried out by using function optimization and automobile modeling design, and the running results show that the algorithm has high optimization performance.
【作者单位】： 合肥工业大学管理学院;铜陵学院信息技术与工程管理研究所;
【基金】：国家自然科学基金Nos.71271072,71331002 中国博士后科学基金No.2014M560508 中央高校基本科研业务费专项资金No.2013HGBH0029 高等学校博士学科点专项科研基金No.20110111110006 安徽省自然科学基金No.1208085MG121 安徽省教育厅重点项目Nos.KJ2012A269,SK2015A537 铜陵学院科研项目No.2014tlxyxs31~~
【分类号】：TP18
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本文编号：1843178