The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications

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• 作者：Christos Tsotskas ; ^{c.tsotskas@cranfield.ac.uk" class="auth_mail} ; Timoleon Kipouros ^{t.kipouros@cranfield.ac.uk" class="auth_mail} ; Anthony Mark Savill ^{mark.savill@cranfield.ac.uk" class="auth_mail}
• 关键词：MOTS2 ; GPU ; CUDA ; metaheuristics ; ZDT ; multi-objective optimization ; real world applications
• 刊名：Procedia Computer Science
• 出版年：2014
• 年：2014
• 卷：29
• 期：Complete
• 页码：2152-2161
• 全文大小：513 K

文摘

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 multi-objective 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.