The journal ranking problem has drawn a great deal of attention from researchers in various fields due to its importance in the evaluation of academic performance. Most previous studies solved the problem with either a subjective approach, based on expert survey metrics, or an objective approach, based on citation-based metrics. Since both have their own advantages and disadvantages, and since they are usually complementary, this work proposes a brand new approach that integrates the two. In this work, we propose the Evolutionary PageRank algorithm, which first uses the PageRank algorithm to evaluate journal prestige and then uses the Multi-Objective Particle Swarm Optimization to balance citation analysis and expert opinion. Experiments evaluating ranking quality were carried out with citation records and experts' surveys to show the effectiveness of the proposed method. The results indicate that the proposed method can improve PageRank journal ranking results.
['Yen-Liang Chen', 'Xiang-Han Chen']
An evolutionary PageRank approach for journal ranking with expert judgements