  Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics
often applied to finding approximate solutions to difficult optimization
problems. Despite being significantly faster than exact methods, the ACOs can
still be prohibitively slow, especially if compared to basic problem-specific
heuristics. As recent research has shown, it is possible to significantly
improve the performance through algorithm refinements and careful parallel
implementation benefiting from multi-core CPUs and dedicated accelerators. In
this paper, we present a novel ACO variant, namely the Focused ACO (FACO). One
of the core elements of the FACO is a mechanism for controlling the number of
differences between a newly constructed and a selected previous solution. The
mechanism results in a more focused search process, allowing to find
improvements while preserving the quality of the existing solution. An
additional benefit is a more efficient integration with a problem-specific
local search.
  Computational study based on a range of the Traveling Salesman Problem
instances shows that the FACO outperforms the state-of-the-art ACOs when
solving large TSP instances. Specifically, the FACO required less than an hour
of an 8-core commodity CPU time to find high-quality solutions (within 1% from
the best-known results) for TSP Art Instances ranging from 100000 to 200000
nodes.
