We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilizes the bandwidth when the network is idle, and limits the op-erating window when the network is congested. We evaluate AFW and compare the total throughput of AFW with that of FeW in different scenarios over chain, grid, random topologies and with hybrid traffics. Extensive simulation through ns2 shows that AFW obtains 5% higher throughput than FeW, whose throughput is significantly higher than that of TCP-Newreno, with limited modi-fications. We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilized the bandwidth when the network is idle, and limits the op-erating window when the network is congested. We evaluate AFW and compare the total throughput of AFW with that of FeW in different scenarios over chain, grid, random topologies and with hybrid traffics. Extensive simulation through ns2 shows that AFW 5% higher throughput than FeW whose throughput is significantly higher than that of TCP-Newreno, with limited modi-fications.