Amoeba-inspired combinatorial optimization machines

Abstract

Domain-specific computing architectures are expected to enable the development of new technologies after the saturation of the performance growth concerning conventional general-purpose computers. One such approach is to develop a combinatorial optimization machine (COM), which enables searching for an approximately optimal solution across a vast number of candidates more rapidly than conventional computers. To achieve the rapidity and optimality of the search, a COM exploits physical processes in specific hardware, exhibiting parallelism and stochasticity. This paper summarizes a unique biologically inspired approach to perform combinatorial optimization, which we have been investigated for over a decade. We first performed a series of biological experiments to evaluate the problem-solving ability of a single-celled amoeba that changes its shape into an optimal one by maximizing its body area for foraging and by minimizing the risk of being exposed to aversive light stimuli. Mathematically modeling the shape-changing dynamics of the amoeba, we formulated two algorithms for solving the traveling salesman problem (TSP) and Boolean satisfiability problem (SAT), named “AmoebaTSP” and “AmoebaSAT,” respectively. AmoebaTSP and AmoebaSAT were implemented using analog and/or digital electronic circuits, to maximize their rapid search performance, by exploiting the parallel and stochastic nature of the circuits. We demonstrated that these COMs, called “electronic amoebae” are advantageous in diverse applications, as they can easily handle arbitrary problem instances of TSP and SAT without any costly preprocessing for problem mapping and their dynamics stabilize only at legal solutions that satisfy all given constraints. As electronic amoebae can be composed of conventional complementary metal-oxide semiconductor devices, they are highly scalable, energy-efficient, and suitable for both cloud- and edge computing applications.