The existing EDA tools address the global placement problem of very large scale integration （VLSI） physical design by minimizing the sum of half-perimeter wirelength （HPWL） under density constraints. However， the non-differentiability of HPWL renders gradient-based advanced optimization methods inapplicable directly to global placement. Consequently， the weighted-average wirelength （WAWL） model is often employed to approximate HPWL， but it struggles to achieve a balance between smoothness and accuracy. This paper introduces an improved self-adaptive weighted-average wirelength （SaWAWL） model. It dynamically adjusts the weighted factors γ for each wire’s actual length， ensuring both smoothness and reduced error in fitting HPWL. The proposed model enhances the quality of global placement. A global placer based on this model is implemented and validated on the DAC 2012 open benchmark. The results indicate a 3.69% reduction in the total sum of half-perimeter wirelength.