A multi-agent approach for building a fuzzy decision support system to assist the SEO process

The process of changing a website position, which affects its visibility in the Internet search engine results, is called Search Engine Optimization (SEO). The modeling of SEO process has been considered a complex problem especially because of the dynamic change of the volume of the information, the increase of the diversity of heterogeneous information and their interactions, the lack of transparency of ranking models and the uncertainty of change in terms of results. Thus, SEO process represents a heterogeneous, distributed, complex, dynamic, adaptive, and evolving system. Therefore, from process and organizational points of view, SEO can be modeled by using multi-agent systems. Further to this proposition, we identified several groups of autonomous agents, representing criteria for the implementation of the SEO process. In this study, we tested several SEO criteria in real conditions on the Google search engine. Results from these experiments permitted us to determine the fuzzy decision rules integrated in a SEO decision support system. These fuzzy decision rules can assist SEO practitioners in their work to take good decisions according to the client's needs and the search engine criteria impact evolution.