Combinatorial optimization problems are ubiquitous, underpinning decision-making in a wide range of fields, including logistics, transportation, communication networks, drug discovery and materials science. However, these problems are computationally expensive, especially for conventional computing systems. As a result, researchers are exploring new computing paradigms capable of solving such problems more efficiently.
Amoeba-inspired method could enable energy-efficient hardware for complex optimization
