The design and implementation of a GPUenabled multi-objective Tabu-search intended for real world and high-dimensional applications

Metaheuristics is a class of approximate methods based on heuristics that can effectively handle real world (usually NP-hard) problems of high-dimensionality with multiple objectives. An existing multiobjective Tabu-Search (MOTS2) has been re-designed by and ported onto Compute Unified Device Architecture (CUDA) so as to effectively deal with a scalable multi-objective problem with a range of decision variables. The high computational cost due to the problem complexity is addressed by employing Graphics Processing Units (GPUs), which alleviate the computational intensity . The main challenges of the re-implementation are the effective communication with the GPU and the transparent integration with the optimization procedures. Finally, future work is proposed towards heterogeneous applications, where improved features are accelerated by the GPUs. © The Authors. Published by Elsevier B.V