發(fā)布時(shí)間：2018-06-24 21:06

  本文選題：選題策略 + 普通遺傳算法��； 參考：《南京師范大學(xué)》2014年博士論文

【摘要】：為了對(duì)量子智能算法用于測驗(yàn)選題的可行性和特性進(jìn)行探索,本文將普通遺傳算法和量子遺傳算法、普通粒子群算法和量子粒子群算法、普通蟻群算法和量子蟻群算法的選題性能進(jìn)行兩兩比較。本研究是基于模擬題庫的研究,采用項(xiàng)目反應(yīng)理論的三參數(shù)邏輯斯蒂模型建立各算法的目標(biāo)函數(shù),各算法得到的選題結(jié)果采用方差分析進(jìn)行差異顯著性檢驗(yàn),分析影響選題結(jié)果的參數(shù)、得到算法的最優(yōu)參數(shù)組合。在三對(duì)算法中選出較優(yōu)的三種算法再進(jìn)行比較,得到本實(shí)驗(yàn)最優(yōu)的選題算法。研究結(jié)果充實(shí)了當(dāng)前的選題策略理論,并首次成功將量子智能算法用于選題。方法論上,不僅在選題領(lǐng)域是個(gè)突破,而且還為人工智能在心理測量中的應(yīng)用擴(kuò)充了新的內(nèi)容。主要研究結(jié)論有以下幾點(diǎn):(1)遺傳算法進(jìn)行本研究的選題實(shí)驗(yàn)的結(jié)果表明:雖然分?jǐn)?shù)線處測驗(yàn)信息量比較大,但是多次選題的標(biāo)準(zhǔn)差也越大,算法不穩(wěn)健。(2)用量子遺傳算法的九種參數(shù)組合進(jìn)行選題實(shí)驗(yàn),進(jìn)行結(jié)果分析和討論后得出:若將分?jǐn)?shù)線處測驗(yàn)信息量指標(biāo)視為最重要,不考慮平坦度和時(shí)間,可選擇種群大小80,迭代次數(shù)為500。若綜合考慮三個(gè)指標(biāo),信息量要盡量大,平坦度也大,且選題時(shí)間短可以選擇種群為80,迭代次數(shù)為300。(3)采用t檢驗(yàn)對(duì)普通遺傳算法和量子遺傳算法的分?jǐn)?shù)線處最大測驗(yàn)信息量、分?jǐn)?shù)線附近信息量平坦度進(jìn)行分析,結(jié)果顯示,在同樣的種群大小和迭代次數(shù)下,普通遺傳算法雖然在大部分情況下的最大測驗(yàn)信息函數(shù)大于量子遺傳算法的。但是,普通遺傳算法選題的分?jǐn)?shù)線附近信息量平坦度值顯著差于量子遺傳算法的。另外,從選題時(shí)間、算法的穩(wěn)健性的角度來看,量子遺傳算法的選題時(shí)間大大短于普通遺傳算法,穩(wěn)健性大大優(yōu)于普通遺傳算法。因此量子遺傳算法用于選題的綜合性能優(yōu)于普通遺傳算法。(4)雖然粒子群算法用于解決其它優(yōu)化問題時(shí),c1和c2取值使得優(yōu)化結(jié)果不同。但是本文首次采用方差分析法進(jìn)行差異顯著性檢驗(yàn),結(jié)果顯示粒子群算法進(jìn)行選題時(shí),c1和c2取不同值對(duì)分?jǐn)?shù)線處信息量、分?jǐn)?shù)線附近信息量平坦度和選題時(shí)間沒有顯著影響,因此可以在[1,4]之間任意取值。(5)量子粒子群算法選題實(shí)驗(yàn)結(jié)果表明,慣性權(quán)重w1和W2對(duì)最大測驗(yàn)信息函數(shù)沒有顯著影響,但是對(duì)信息量平坦度有顯著影響。因此選題時(shí)要考慮其取值,量子粒子群的最佳參數(shù)組合有以下兩種情況:若是將分?jǐn)?shù)線處測驗(yàn)信息量和信息量平坦度指標(biāo)視為最重要時(shí):W1的最佳取值為1.2,W2為0.3,粒子數(shù)量取40,迭代次數(shù)為700。若是綜合考慮三個(gè)指標(biāo)的重要性時(shí),W1的最佳取值為1.2,W2為0.3,粒子數(shù)量取40,迭代次數(shù)為300。(6)采用t檢驗(yàn)對(duì)兩種算法的分?jǐn)?shù)線處最大測驗(yàn)信息量、分?jǐn)?shù)線附近信息量平坦度進(jìn)行分析,在分?jǐn)?shù)線附近信息量平坦度上,兩種算法沒有顯著差異,但是量子粒子群算法在最大信息量上大部分情況下(5種)顯著高于粒子群算法,其選題時(shí)間,選題穩(wěn)健度方面都比粒子群算法略勝一籌,因此,可以認(rèn)為量子粒子群用于基于項(xiàng)目反應(yīng)理論的HSK選題時(shí),選題效果要?jiǎng)俪隽Ｗ尤骸?7)量子蟻群算法選題結(jié)果表明,若是將分?jǐn)?shù)線處的測驗(yàn)信息量的重要性視為最大,則選擇第一種參數(shù)組合(ρ=0.1,Q=150, m-70, d=360)。若是綜合考慮三個(gè)選題指標(biāo),則第五種(ρ=0. 5,Q=250,m=50,d=200)參數(shù)組合下的選題結(jié)果最優(yōu)。(8)采用t檢驗(yàn)對(duì)蟻群算法和量子蟻群算法的分?jǐn)?shù)線處最大測驗(yàn)信息量、分?jǐn)?shù)線附近信息量平坦度進(jìn)行分析,結(jié)果顯示量子蟻群算法在九種參數(shù)條件下,分?jǐn)?shù)線處最大測驗(yàn)信息量顯著優(yōu)于普通蟻群算法,兩種算法的信息量平坦度沒有顯著差異;在算法的穩(wěn)健性方面,對(duì)各算法下選題成卷20次的分?jǐn)?shù)線處最大測驗(yàn)信息量的標(biāo)準(zhǔn)差,試卷的區(qū)分度、難度、猜測度的標(biāo)準(zhǔn)差進(jìn)行分析,結(jié)果顯示在大部分情況下,量子蟻群算法的穩(wěn)健性都由于普通蟻群算法。量子蟻群算法明顯優(yōu)于普通蟻群之處是選題時(shí)間大大短于蟻群算法。因此,綜合考慮各方面的算法評(píng)價(jià)指標(biāo),量子蟻群算法優(yōu)于普通蟻群算法。(9)對(duì)量子遺傳、量子粒子群、量子蟻群三種算法用兩種方式進(jìn)行了比較,兩種方法的結(jié)果都表明,量子遺傳算法雖然不是在所有評(píng)價(jià)指標(biāo)上都為最優(yōu),但是在大部分評(píng)價(jià)指標(biāo)上都顯示為最優(yōu),特別是其選題時(shí)間要遠(yuǎn)遠(yuǎn)小于其他幾種算法,因此量子遺傳算法為本次選題的最優(yōu)算法。
[Abstract]:In order to explore the feasibility and characteristics of quantum intelligent algorithm used to test topic selection, this paper compares the performance of general genetic algorithm and quantum genetic algorithm, ordinary particle swarm algorithm and quantum particle swarm algorithm, common ant colony algorithm and quantum ant colony algorithm. This study is based on the research of analog question bank and adopts the project. The three parameter logistic model of the reaction theory establishes the objective function of each algorithm. The results of each algorithm are tested by variance analysis, analyze the parameters that affect the results of the selected topic, and get the optimal combination of the algorithm. In the three algorithm, three better algorithms are selected to compare, and the optimal experiment is obtained. The research results enrich the current topic selection strategy theory and apply the quantum intelligent algorithm to the topic selection for the first time. Methodology, not only is a breakthrough in the topic selection field, but also expands the new content for the application of artificial intelligence in psychological measurement. The main research results are as follows: (1) genetic algorithm for this study The result of the selected experiment shows that, although the amount of information at the score line is relatively large, the standard deviation of the selected topic is also bigger and the algorithm is not robust. (2) the experiment is carried out with nine parameters combination of quantum genetic algorithm, and the results are analyzed and discussed. And time, the size of the population is 80 and the number of iterations is 500.. If three indexes are taken into consideration, the amount of information should be as large as possible and the degree of flatness is large, and the selection of the selected population is 80 and the number of iterations is 300. (3). The maximum test information at the fractional line at the common genetic algorithm and the quantum genetic algorithm and the letter near the fractional line are used by the t test. The results show that, under the same population size and the number of iterations, the maximum information function of the general genetic algorithm is larger than the quantum genetic algorithm in most cases. However, the level of information level near the score line of the general genetic algorithm is significantly worse than the quantum genetic algorithm. According to the selection time and robustness of the algorithm, the selection time of the quantum genetic algorithm is much shorter than the ordinary genetic algorithm, and the robustness is much better than the ordinary genetic algorithm. Therefore, the comprehensive performance of the quantum genetic algorithm is better than the ordinary genetic algorithm. (4) although the particle swarm optimization algorithm is used to solve other optimization problems, the C1 and C2 values are obtained. The optimization results are different. But the variance analysis method is used for the first time to test the difference saliency. The results show that when the particle swarm optimization is selected, C1 and C2 have no significant influence on the amount of information at the fraction line, the level of information flatness near the fractional line and the selection time, so it can be arbitrarily taken between [1,4]. (5) quantum particles. The experimental results of subgroup algorithm show that the inertia weight W1 and W2 have no significant influence on the maximum test information function, but have significant influence on the flatness of the information quantity. Therefore, the selection of the selected topic should be taken into consideration. The best combination of the quantum particle swarm has the following two cases: if the amount of information and the amount of information at the fraction line is flatness index The best value is 1.2, the best value for W1 is 1.2, the W2 is 0.3, the number of particles is 40, and the number of iterations is 700. if the importance of three indexes is taken into consideration. The best value of the W1 is 1.2, W2 is 0.3, the number of particles is 40, the number of iterations is 300. (6), the maximum test information at the fractional line of the two algorithms and the information near the fractional line are used by t test. There is no significant difference between the two algorithms on the flatness of the amount of information near the fractional line, but the quantum particle swarm algorithm is significantly higher than the particle swarm optimization in most cases (5 kinds) of the maximum information. The time of selection and the robustness of the selected topic are all better than that of the particle swarm optimization. Therefore, the quantum particle can be considered as a quantum particle. When the group is used to select the HSK topic based on the project response theory, the effect of the selected topic is better than the particle swarm. (7) the results of the quantum ant colony algorithm show that the first parameter combination (rho =0.1, Q=150, m-70, d= 360) is selected if the importance of the quantity of test information at the fractional line is considered as the maximum. If the three selection indexes are taken into consideration, the fifth species (P =0. 5) The optimal selection results under the combination of Q=250, m=50 and d=200 are the best. (8) the maximum test information at the score line at the ant colony algorithm and the quantum ant colony algorithm and the flatness of the information quantity near the fractional line are analyzed by using the t test. The results show that the quantum ant colony algorithm is significantly better than the ordinary ant under the nine parameters. In the group algorithm, there is no significant difference in the flatness of the amount of information between the two algorithms; in the robustness of the algorithm, the standard deviation of the maximum test information at the score line of the 20 times under each algorithm, the degree of the test paper, the difficulty and the standard deviation of the guess measure are analyzed. The results show the robustness of the quantum ant colony algorithm in most cases. Because of the common ant colony algorithm, the quantum ant colony algorithm is obviously better than the common ant colony, and the selection time is much shorter than the ant colony algorithm. Therefore, the quantum ant colony algorithm is superior to the common ant colony algorithm considering the evaluation index of all aspects. (9) three algorithms are compared in two ways for quantum genetic, quantum particle swarm and quantum ant colony algorithm, two The results of the method show that, although the quantum genetic algorithm is not the best in all evaluation indexes, it is shown to be the best in most evaluation indexes, especially the selection time of the quantum genetic algorithm is far smaller than that of the other several algorithms, so the quantum genetic algorithm is the best algorithm for this topic.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：B841

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張晉軍;;新漢語水平考試(HSK)題庫建設(shè)之我見[J];中國考試;2013年04期

2 李欣然;靳雁霞;;一種求解組卷問題的量子粒子群算法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2012年07期

3 楊榮華;劉建華;;量子粒子群算法求解整數(shù)規(guī)劃的方法[J];科學(xué)技術(shù)與工程;2011年33期

4 唐世文;;基于蟻群算法的智能組卷算法研究[J];計(jì)算機(jī)時(shí)代;2011年10期

5 黃寶玲;;自適應(yīng)遺傳算法在智能組卷中的應(yīng)用[J];計(jì)算機(jī)工程;2011年14期

6 胡海峰;何偉娜;;改進(jìn)小生境遺傳算法在組卷策略中的應(yīng)用研究[J];喀什師范學(xué)院學(xué)報(bào);2011年03期

7 周艷聰;劉艷柳;顧軍華;;小生境自適應(yīng)遺傳模擬退火智能組卷策略研究[J];小型微型計(jì)算機(jī)系統(tǒng);2011年02期

8 馬躍亮;靳志強(qiáng);孫晨霞;;一種改進(jìn)的多目標(biāo)粒子群組卷算法[J];微型機(jī)與應(yīng)用;2010年23期

9 劉志雄;梁華;;粒子群算法中隨機(jī)數(shù)參數(shù)的設(shè)置與實(shí)驗(yàn)分析[J];控制理論與應(yīng)用;2010年11期

10 陳梅;王翠茹;;基于IRT指導(dǎo)的雙界分點(diǎn)測驗(yàn)編制的算法設(shè)計(jì)[J];中央民族大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年03期

相關(guān)碩士學(xué)位論文 前1條

1 馬德良;基于改進(jìn)遺傳算法的智能組卷方法研究[D];國防科學(xué)技術(shù)大學(xué);2010年

，

本文編號(hào)：2062954