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 that 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

PILS is an optimization strategy that utilizes pattern mining to explore high-order local-search neighborhoods, improving solution efficiency through specific moves based on stored frequent patterns. It introduces a new paradigm of local search that enhances classical search approaches within controllable computational time.