Memcomputing Implementation of Ant Colony Optimization (original) (raw)

Abstract

We report on similarities between memcomputing with memristive networks and ant colony optimization. In particular, we show that one can design memristive networks to solve short-path optimization problems in a way similar to that done by ant-colony optimization algorithms. By employing appropriate memristive elements one can demonstrate an almost one-to-one correspondence between memcomputing and ant colony optimization approaches. However, the memristive network has the capability of finding the solution in one deterministic step, compared to the stochastic multi-step ant-colony optimization. This result is a first step in the direction of implementing in hardware, with nanoscale devices, this and possibly other swarm intelligence algorithms that are presently explored.

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Notes

  1. One can use a single physical memristive device to represent path 2 encoding path 2 length in its parameters.
  2. Different values of \(\alpha \) and \(\beta \) could be realized with different types (models) of memristive devices.

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Acknowledgments

This work has been partially supported by NSF Grant ECCS-1202383 and the Center for Magnetic Recording Research at UCSD. The hospitality of the Aspen Center for Physics (supported by NSF Grant PHY-1066293), where part of this work was done, is also acknowledged.

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Authors and Affiliations

  1. Department of Physics and Astronomy and Smart State Center for Experimental Nanoscale Physics, University of South Carolina, Columbia, SC, 29208, USA
    Yuriy V. Pershin
  2. Department of Physics, University of California, La Jolla, San Diego, CA, 92093-0319, USA
    Massimiliano Di Ventra

Authors

  1. Yuriy V. Pershin
  2. Massimiliano Di Ventra

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Correspondence toYuriy V. Pershin.

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Pershin, Y.V., Di Ventra, M. Memcomputing Implementation of Ant Colony Optimization.Neural Process Lett 44, 265–277 (2016). https://doi.org/10.1007/s11063-016-9497-y

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