Accelerated Numerical Optimization with Explicit Consideration of Model Constraints

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 796)


Population based metaheuristics can benefit from parallelization in order to address complex numerical optimization problems. Typical realistic problems usually involve non-linear functions, integer variables and many constraints, making the identification of optimal solutions mathematically challenging and computationally expensive. In this work, a parallelized version of the Particle Swarm Optimization technique is proposed, whose main contribution is the explicit consideration of constraints. The implementation is tested on a classic set of optimization problems. Speedups up to 101x were obtained using a single GPU on a standard PC using the Py-Cuda technology.


Numerical optimization Particle swarm optimization GPU 



This research was partially supported by grants from Consejo Nacional de InvestigacionesCientíficas y Técnicas (CONICET) and Universidad Tecnológica Nacional (UTN) of Argentina. The authors also gratefully acknowledge the support of NVIDIA Corporation with the donation of the TITAN X GPU used in this research.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.PLAPIQUI (CONICET-UNS)Bahía BlancaArgentina
  2. 2.UTN-FRBBBahía BlancaArgentina

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