Rank Aggregation Using Moth Search for Web

Abstract

Metasearch engines always play a crucial role to provide useful information for the requested query. At the hinder end, the rank aggregation (RA) module is perhaps the most important component which merges the output derived from distinct search engines. But the primary goal of this problem is to assign a relevance rank to similar documents from different search engines in order to select the best optimized document. With this application in mind, we have proposed moth search algorithm (MSA)-based approach along with two distance measure methods. Thus, Spearman’s footrule and Kendall tau distance measures are optimized which are further applied to assign ranks to the documents by different rank aggregation methods. Experimentally, it has been proved that MSA approach outperformed than conventional genetic algorithm.