本文選題：交互式蟻群優(yōu)化 + 蟻群優(yōu)化　； 參考：《計算機科學與探索》2016年12期

【摘要】：交互式蟻群優(yōu)化(interactive ant colony optimization,i ACO)是一種利用人來評價解的優(yōu)劣而進行系統(tǒng)優(yōu)化的技術,可以求解性能指標不能或者難以數量化的優(yōu)化問題。分析了交互式蟻群優(yōu)化模型面臨的研究困難。針對Tanabe等人提出的交互式螞蟻算法性能不足的問題,提出利用全局歷史最優(yōu)解進行信息素的更新,并將信息素限定在一定區(qū)間內的改進交互式蟻群優(yōu)化算法,從人機交互角度討論了解的構造方法和人的評價策略。最后,利用函數優(yōu)化和汽車造型設計進行了實驗,運行結果表明算法具有較高優(yōu)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.
【作者單位】： 合肥工業(yè)大學管理學院;銅陵學院信息技術與工程管理研究所;
【基金】：國家自然科學基金Nos.71271072,71331002 中國博士后科學基金No.2014M560508 中央高校基本科研業(yè)務費專項資金No.2013HGBH0029 高等學校博士學科點專項科研基金No.20110111110006 安徽省自然科學基金No.1208085MG121 安徽省教育廳重點項目Nos.KJ2012A269,SK2015A537 銅陵學院科研項目No.2014tlxyxs31~~
【分類號】：TP18
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本文編號：1843178