We address a practice-relevant optimization problem: optimizing multi-product batch plants, with a real-world use case study – optimal design of chemical-engineering systems. Our contribution is a novel approach to parallelizing this optimization problem on GPU (Graphics Processing Units) by combining two metaheuristics – Simulated Annealing (SA) and Ant Colony Optimization (ACO). We improve the implementation performance by tuning particular parameters of the ACO metaheuristic. Our tuning approach improves on the previous methods in two respects: (1) we do not have to rely on additional mechanisms like fuzzy logic or algorithms for online tuning; and (2) we use the high computation performance of GPU to speedup the tuning process. By parallelizing the tuning process on modern GPUs, we allow the user to experiment with large volumes of input data and find the optimal values of the ACO parameters in feasible time. Our experiments on NVIDIA GPU show the efficiency of our approach to parameter tuning for the ACO metaheuristic.

我们解决与实践相关的优化问题：通过实际应用案例研究优化多产品批处理工厂–化学工程系统的优化设计。我们的贡献是通过结合两种元启发法-模拟退火（SA）和蚁群优化（ACO）来并行化GPU（图形处理单元）上的优化问题的新颖方法。我们通过调整ACO元启发式算法的特定参数来提高实现性能。我们的调整方法在两个方面对以前的方法进行了改进：（1）我们不必依靠其他机制（例如模糊逻辑或算法）进行在线调整；（2）我们使用GPU的高计算性能来加速调整过程。通过并行化现代GPU上的调整过程，我们允许用户尝试大量输入数据，并在可行的时间内找到ACO参数的最佳值。我们在NVIDIA GPU上进行的实验表明，我们针对ACO元启发式算法进行参数调整的方法非常有效。