This paper describes the use of Renyi’sentropy as a way to improve the convergence timeof the Population-Based Incremental Learning (PBIL)optimization algorithm. As a case study, the algorithmwas used in a hierarchical wireless network-on-chip(WiNoC) for the sake of performing the optimal taskmapping of applications. Two versions of Renyi’s entropyare used and compared to the more traditional Shannonformulation. The obtained results are promising andsuggest that Renyi’s entropy may help to reduce thePBIL convergence time, without degrading the quality ofthe found solutions.