Efficient GPU-parallelization of batch plants design using metaheuristics with parameter tuning

Abstract 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.