A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
FANT: Fast ant system
Program optimization carving for GPU computing
Journal of Parallel and Distributed Computing
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
Genetic Programming and Evolvable Machines
GPU-Based Road Sign Detection Using Particle Swarm Optimization
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
MAX-MIN Ant System on GPU with CUDA
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform
Journal of Parallel and Distributed Computing
ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Hybrid metaheuristics are powerful methods for solving complex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a result, 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.