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00925njm 22002777a 4500 dc Rey, Antón author Prieto, Manuel author Gómez, Juan Ignacio author Tenllado Van Der Reijden, Christian Tomás author Hidalgo, Juna Ignacio author 2018 This paper exposes a new hybrid approach based on Ant Colony Optimization heuristics, Route First-Cluster Second methods and Local search procedures, combined to generate high quality solutions for the Vehicle Routing Problem. This method uses the parallel computing power of modern general purpose GPUs and multicore CPUs, outperforming current ACO-based VRP solvers and showing to be a competitive approach compared to other high performing metaheuristic solvers. Rey, A., Prieto, M., Gómez, J.I., Tenllado, C., Hidalgo, J.I. (2018). A CPU-GPU Parallel Ant Colony Optimization Solver for the Vehicle Routing Problem. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. 978-3-319-77537-1 10.1007/978-3-319-77538-8_44 https://hdl.handle.net/20.500.14352/97832 https://doi.org/10.1007/978-3-319-77538-8_44 https://www.evostar.org/2018/cfp_evoapps.php https://rdcu.be/dxpO8 A CPU-GPU Parallel Ant Colony Optimization Solver for the Vehicle Routing Problem