Population-Based Iterated Local Search: Restricting Neighborhood Search by Crossover

Abstract

Iterated local search (ILS) is a powerful meta-heuristic algorithm applied to a large variety of combinatorial optimization problems. Contrary to evolutionary algorithms (EAs) ILS focuses only on a single solution during its search. EAs have shown however that there can be a substantial gain in search quality when exploiting the information present in a population of solutions. In this paper we propose the use of a population for ILS. We define the general form of the resulting meta-heuristic, called population-based iterated local search (PILS). PILS is a minimal extension of ILS that uses previously generated solutions in the neighborhood of the current solution to restrict the neighborhood search by ILS. This neighborhood restriction is analogous to the way crossover preserves common substructures between parents when generating offspring. To keep the discussion concrete, we discuss a specific instantiation of the PILS algorithm on a binary trap function.