PILS: Exploring high-order neighborhoods by pattern mining and injection
  We introduce pattern injection local search (PILS), an optimization strategy
that uses pattern mining to explore high-order local-search neighborhoods, and
illustrate its application on the vehicle routing problem. PILS operates by
storing a limited number of frequent patterns from elite solutions. During the
local search, each pattern is used to define one move in which 1) incompatible
edges are disconnected, 2) the edges defined by the pattern are reconnected,
and 3) the remaining solution fragments are optimally reconnected. Each such
move is accepted only in case of solution improvement. As visible in our
experiments, this strategy results in a new paradigm of local search, which
complements and enhances classical search approaches in a controllable amount
of computational time. We demonstrate that PILS identifies useful high-order
moves (e.g., 9-opt and 10-opt) which would otherwise not be found by
enumeration, and that it significantly improves the performance of
state-of-the-art population-based and neighborhood-centered metaheuristics.
