Abstract: Dynamic Optimization of ExpertBase Based on Peer review is a widely recognized approach.It is an expert review process and the performance is greatly dependent on the selection of experts that is done by using an expertbase.The information in an expertbase is often obtained by statically evaluating the experts' performance, but not dynamically updated.In this paper, the drawbacks are analyzed for such a review process.Based on the analysis, to solve this problem, we propose to dynamically update the expertbase by tracking their realtime performances.With time series multiattribute decision making, dynamic experts' work performance evaluation is presented by choosing the appropriate time scale.Thus, the expertbase can be dynamically updated based on not only the static indexes but also their changes.In other words, the expertbase is dynamically optimized.