<p>The Wind Driven Optimization (WDO) algorithm is a metaheuristic technique inspired by atmospheric flow dynamics. Although structurally simple, WDO suffers from static search behavior and diversity loss caused by global wind-speed-based sorting. It also lacks fine-grained local search capabilities, which limit its performance in complex, high-dimensional, and constrained tasks. To address these issues, this study proposes an improved optimization model by integrating WDO, Beluga Whale Optimization (BWO), and the Golden Sine Algorithm (GSA). WDO removes wind-speed sorting to preserve diversity and enable multi-strategy fusion. Subsequently, WDO is integrated with BWO to enhance global search capabilities, while GSA is adopted for fine-grained local exploitation. Consequently, the integrated WDO-BWO-GSA approach is applied to estimate the optimal hardware-software (HW-SW) partitioning. Experiments on 24 benchmark functions including both ablation and comparative studies were conducted to evaluate the effectiveness of the proposed algorithm. The results confirmed that the integrated WDO-BWO-GSA significantly enhanced global exploration and local exploitation. Comparative results further demonstrated that the proposed integrated WDO-BWO-GSA outperformed WDO, BWO, Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and GSA, achieving average convergence speed improvements of 62.07%, 54.28%, 62.31%, 70.69%, 72.26%, and 62.19%, respectively. Additionally, on a HW-SW partitioning task, it surpassed the Complex-valued encoding WDO (CWDO) by 59.55% in convergence speed, while also delivering superior solution quality. These results confirmed the robustness and adaptability of the proposed integrated WDO-BWO-GSA in solving complex partitioning optimization problems.</p>