本文關(guān)鍵詞： 信息檢索 數(shù)據(jù)融合 線性組合法 權(quán)重分配 差分進化算法 粒子群算法 自適應(yīng)交替粒子群差分進化算法　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著以網(wǎng)絡(luò)技術(shù)為核心的現(xiàn)代信息技術(shù)的不斷發(fā)展,如何幫助用戶從互聯(lián)網(wǎng)大量信息中迅速準(zhǔn)確地獲取用戶需要的信息是信息檢索的首要問題。數(shù)據(jù)融合技術(shù)能夠?qū)⒉煌臋z索系統(tǒng)所提交的檢索結(jié)果進行組合從而得到一個新的檢索結(jié)果。之前的的研究結(jié)果表明,數(shù)據(jù)融合技術(shù)能夠有效地提高檢索結(jié)果的性能。本文主要研究數(shù)據(jù)融合技術(shù)中的線性組合法,著重探討如何采用智能優(yōu)化算法解決線性組合法的權(quán)重分配問題。論文的主要工作如下:(1)本文探討了基于差分進化算法和基于粒子群算法的權(quán)重分配策略。在上述兩種優(yōu)化算法的基礎(chǔ)上,探討了基于自適應(yīng)交替的粒子群差分進化優(yōu)化算法權(quán)重分配策略。該策略使用自適應(yīng)的概率交替使用差分進化算法和粒子群算法對系統(tǒng)權(quán)重進行優(yōu)化,可以有效避免粒子群算法已陷入局部極值的特點以及進一步強化差分進化算法的收斂能力。據(jù)我們所知,自適應(yīng)交替粒子群差分進化算法是首次被應(yīng)用到此類問題中。(2)為測試上述算法的有效性,采用TREC 2004 Robust Task數(shù)據(jù)集對不同數(shù)量的成員系統(tǒng)進行融合實驗。實驗結(jié)果表明,基于差分進化算法和基于粒子群算法的權(quán)重分配策略所得到的融合結(jié)果性能提升較為明顯,而基于自適應(yīng)交替差分進化粒子群優(yōu)化算法的權(quán)重分配策略所得到的融合結(jié)果提升最為顯著。(3)本文對所探討的的三種融合方法在訓(xùn)練權(quán)重時所耗時間進行了比較。其中基于粒子群算法的權(quán)重分配策略訓(xùn)練耗時最少,基于自適應(yīng)交替的粒子群差分進化優(yōu)化算法權(quán)重分配策略次之,而基于差分進化算法的權(quán)重分配策略訓(xùn)練耗時最長。本文對已有的數(shù)據(jù)融合方法進行了簡要的說明,從智能優(yōu)化算法的角度提出了一種新的線性組合法權(quán)重分配策略,并通過實驗比較這些融合方法的有效性和運行效率。實驗結(jié)果表明,兼顧時間和性能,基于自適應(yīng)交替粒子群差分進化優(yōu)化算法權(quán)重分配策略能夠有效地提升融合結(jié)果的性能。
[Abstract]:With the network technology as the core of the development of modern information technology. How to help users quickly and accurately obtain the information they need from a large amount of information on the Internet is the most important problem in information retrieval. Data fusion technology can combine the retrieval results submitted by different retrieval systems. Get a new search result. Previous research results show. Data fusion technology can effectively improve the performance of retrieval results. This paper mainly discusses how to use intelligent optimization algorithm to solve the weight assignment problem of linear combination method. The main work of this paper is as follows: 1). This paper discusses the weight allocation strategy based on differential evolution algorithm and particle swarm optimization algorithm. This paper discusses the weight allocation strategy of particle swarm optimization algorithm based on adaptive alternation, which uses adaptive probability alternately to optimize the weight of the system using differential evolution algorithm and particle swarm optimization algorithm. It can effectively avoid the characteristic that particle swarm optimization has fallen into local extremum and further enhance the convergence ability of differential evolution algorithm. Adaptive alternative Particle Swarm Optimization differential Evolution algorithm (APSO) is the first time to be applied to this kind of problem. TREC 2004 Robust Task dataset is used to perform fusion experiments on different number of member systems. The experimental results show that. The performance of the fusion algorithm based on differential evolution algorithm and particle swarm optimization algorithm is improved obviously. However, the fusion result obtained by the weight allocation strategy based on adaptive alternative differential evolution particle swarm optimization algorithm is the most significant. In this paper, we compare the time of the three fusion methods in training weight, and the training time of weight allocation strategy based on particle swarm optimization is the least. The weight allocation strategy of particle swarm optimization algorithm based on adaptive alternation is the second. The training time of weight allocation strategy based on differential evolution algorithm is the longest. In this paper, the existing data fusion methods are briefly explained. From the point of view of intelligent optimization algorithm, a new weight allocation strategy of linear combination method is proposed, and the effectiveness and efficiency of these fusion methods are compared by experiments. The experimental results show that both time and performance are taken into account. The weight allocation strategy based on adaptive alternative particle swarm optimization algorithm can effectively improve the performance of fusion results.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP202

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