Parallelization strategies for hybrid metaheuristics using a single GPU and multi-core resources

Hybrid metaheuristics are powerful methods for solving com- plex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a re- sult, the use of GPU computing has been recognized as a major way to speed up the search process. However, most GPU-accelerated algorithms of the literature do not take benefits of all the available CPU cores. In this paper, we introduce a new guideline for the design and implementation of effective hybrid metaheuristics using heterogeneous resources