Memcomputing and Swarm Intelligence
This work enables nanoscale hardware implementations for swarm intelligence algorithms, potentially benefiting scheduling and robotics, but it is incremental as it builds on existing methods.
The paper tackles the problem of solving short-path optimization by establishing a correspondence between memcomputing and ant colony optimization, showing that memristive networks can find solutions in one deterministic step compared to the stochastic multi-step approach of ant colony algorithms.
We explore the relation between memcomputing, namely computing with and in memory, and swarm intelligence algorithms. In particular, we show that one can design memristive networks to solve short-path optimization problems that can also be solved by ant-colony 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 paves the way for nanoscale hardware implementations of several swarm intelligence algorithms that are presently explored, from scheduling problems to robotics.