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

PILS: Exploring high-order neighborhoods by pattern mining and injection