本文選題：交互式蟻群優(yōu)化 + 蟻群優(yōu)化��； 參考：《計(jì)算機(jī)科學(xué)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(lái)評(píng)價(jià)解的優(yōu)劣而進(jìn)行系統(tǒng)優(yōu)化的技術(shù),可以求解性能指標(biāo)不能或者難以數(shù)量化的優(yōu)化問(wèn)題。分析了交互式蟻群優(yōu)化模型面臨的研究困難。針對(duì)Tanabe等人提出的交互式螞蟻算法性能不足的問(wèn)題,提出利用全局歷史最優(yōu)解進(jìn)行信息素的更新,并將信息素限定在一定區(qū)間內(nèi)的改進(jìn)交互式蟻群優(yōu)化算法,從人機(jī)交互角度討論了解的構(gòu)造方法和人的評(píng)價(jià)策略。最后,利用函數(shù)優(yōu)化和汽車(chē)造型設(shè)計(jì)進(jìn)行了實(shí)驗(yàn),運(yùn)行結(jié)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(xué)管理學(xué)院;銅陵學(xué)院信息技術(shù)與工程管理研究所;
【基金】：國(guó)家自然科學(xué)基金Nos.71271072,71331002 中國(guó)博士后科學(xué)基金No.2014M560508 中央高�；究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金No.2013HGBH0029 高等學(xué)校博士學(xué)科點(diǎn)專(zhuān)項(xiàng)科研基金No.20110111110006 安徽省自然科學(xué)基金No.1208085MG121 安徽省教育廳重點(diǎn)項(xiàng)目Nos.KJ2012A269,SK2015A537 銅陵學(xué)院科研項(xiàng)目No.2014tlxyxs31~~
【分類(lèi)號(hào)】：TP18
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本文編號(hào)：1843178