Compared with noisy chaotic neural networks (NCNNs), hysteretic noisy chaotic neural networks (HNCNNs) are more likely to exhibit better optimization performance at higher noise levels, but behave worse at lower noise levels. In order to improve the optimization performance of HNCNNs, this paper presents a novel noise-tuning-based hysteretic noisy chaotic neural network (NHNCNN). Using a noise tuning factor to modulate the level of stochastic noises, the proposed NHNCNN not only balances stochastic wandering and chaotic searching, but also exhibits stronger hysteretic dynamics, thereby improving the optimization performance at both lower and higher noise levels. The aim of the broadcast scheduling problem (BSP) in wireless multihop networks (WMNs) is to design an optimal time-division multiple-access frame structure with minimal frame length and maximal channel utilization. A gradual NHNCNN (G-NHNCNN), which combines the NHNCNN with the gradual expansion scheme, is applied to solve BSP in WMNs to demonstrate the performance of the NHNCNN. Simulation results show that the proposed NHNCNN has a larger probability of finding better solutions compared to both the NCNN and the HNCNN regardless of whether noise amplitudes are lower or higher.