Particle Swarm Optimization with Discrete Recombination: An Online Optimizer for Evolvable Hardware

  • Authors:

  • Jorge Pena;Andres Upegui;Eduardo Sanchez

  • Affiliations:

  • Universita della Svizzera Italiana - USI, Switzerland;Ecole Polytechnique Federale de Lausanne - EPFL, Switzerland;Ecole Polytechnique Federale de Lausanne - EPFL, Switzerland

  • Venue:
  • AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
  • Year:
  • 2006

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Abstract

Self-reconfigurable adaptive systems have the possibility of adapting their own hardware configuration. This feature provides enhanced performance and flexibility, reflected in computational cost reductions. Self-reconfigurable adaptation requires powerful optimization algorithms in order to search in a space of possible hardware configurations. If such algorithms are to be implemented on chip, they must also be as simple as possible, so the best performance can be achieved with the less cost in terms of logic resources, convergence speed, and power consumption. This paper presents an hybrid bio-inspired optimization technique that introduces the concept of discrete recombination in a particle swarm optimizer, obtaining a simple and powerful algorithm, well suited for embedded applications. The proposed algorithm is validated using standard benchmark functions and used for training a neural network-based adaptive equalizer for communications systems.