Parallel computing involving central processing units (CPUs) and graphics processing units (GPUs), known as heterogenous computing, can greatly improve computational efficiency across various scientific and engineering domains. The heterogeneous computing platform best suits algorithms requiring sequential and parallel processing. A GPU with shallow pipelines, short caches, and numerous threads doing sequential tasks is connected to a CPU with a more complex hardware design via a PCIe bus. It is crucial to note that the complexity of heterogeneous computing and the time cost of transporting input data to the GPU for processing and obtaining output results from the GPU is useless if the time cost is greater than the savings from processing the data on the GPU. Therefore, to take advantage of heterogeneous computing, the best candidates for optimization problems are those that compute on the same data multiple times. In this research, the researchers proposed the concept of “solution batching” to take advantage of fewer data transfer operations between the CPU and GPU. The quadratic assignment problem (QAP) is utilized to verify the suitability of the innovative technique in improving heterogeneous computing. Additionally, the nature of the QAP problem and solution batching, the obstacles and difficulties, and the possibility of implementation will also be covered in this investigation.