Abstract：To automatically merge the result from multiple independent research engines (IREs) is a key component of the metasearch engine development and it is problem in distributed information retrieval applications as well. After testing a variety of existing result merging algorithms for multiple IRE results, a discrete particle swarm algorithm (DPSA) is proposed for further optimizing a group of merging results produced by other result merging algorithms.The experimental results show that the DPSA generally outperforms all the other result merging algorithms. It usually has better adaptability in application for not having to take into account the usefulness weights of IRE results and the overlap rate among different IRE results of a query. Compared to other result merging algorithms, the recognition precision of DPSA increases nearly 20%, while the precision standard deviation for different queries decreases about 50%.
[1] Yuan Fuyong,Wang Jindong.An Implemented Rank Merging Algorithm for Meta Search Engine // Proc of the ICRCCS International Conference on Research Challenges in Computer Science.Shanghai,China,2009: 191-193 [2] Ganzha M,Paprzycki M,Stadnik J.Combining Information from Multiple Search Engines-Preliminary Comparison.Information Science: An International Journal,2010,180(10): 1908-1923 [3] Wu Shengli,McClean S.Result Merging Methods in Distributed Information.Journal of Information Retrieval,2007,10(3): 297-319 [4] Lu Yiyao,Meng Weiyi,Shu Liangcai,et al.Evaluation of Result Merging Strategies for Metasearch Engines // Proc of the 6th International Conference on Web Information System Engineering.New York,USA,2005: 53-66 [5] Wu Shengli,Crestani F.Shadow Document Methods of Results Merging // Proc of the 19th ACM Symposium on Applied Computing.Nicosia,Cyprus,2004: 1067-1072 [6] Yang Qingyun,Wang Chunjie,Zhang Changsheng.An Efficient Discrete Particle Swarm Algorithm for Task Assignment Problems // Proc of the IEEE International Conference on Granular Computing.Changchun,China,2009: 686-690 [7] Yu Linli,Cai Zixing.Multiple Optimization Strategies for Improving Hybrid Discrete Particle Swam.Journal of Central South University: Natural Science,2009,40(4): 1048-1053 (in Chinese) (余伶俐,蔡自兴.改进混合离散粒子群的多种优化策略算法.中南大学学报:自然科学版,2009,40(4): 1048-1053) [8] Nie Duxian,Yue Liguo,Wen Youwei.Applying Particle Swarm Optimization Algorithm to Select Regularization Parameter.Journal of Computer Engineering and Applications,2009,45(12): 195-197 (in Chinese) (聂笃宪,袁利国,文有为.应用粒子群优化算法选择正则化参数.计算机工程与应用,2009,45(12): 195-197) [9] Calderero F,Marques F.Region Merging Techniques Using Information Theory Statistical Measures.IEEE Trans on Image Processing,2010,19(6): 1567-1586 [10] Cao Zhe,Tao Qin,Liu Tieyan,et al.Learning to Rank: From Pairwise Approach to Listwise Approach // Proc of the 24th International Conference on Machine Learning.Corvallis,USA,2007: 129-136 [11] Xia Fen,Liu Tieyan,Wang Jun,et al.Listwise Approach to Learning to Rank-Theory and Algorithm // Proc of the 25th International Conference on Machine Learning.Helsinki,Finland,2008: 1192-1199 [12] Guo Qinglin,Li Yanmei,Tang Qi.Similarity Computing of Documents Based on VSM.Journal of Application Research of Computers,2008,25(11): 3256-3258 (in Chinese) (郭庆琳,李艳梅,唐 琦.基于VSM的文本相似度计算的研究.计算机应用研究,2008,25(11): 3256-3258)
2012, Vol. 25 
