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This paper presents a new methodology that exploits specific characteristicsfrom the fitness landscape. In particular, we are interested in the property ofneutrality, that deals with the fact that the same fitness value is assigned tonumerous solutions from the search space. Many combinatorial optimizationproblems share this property, that is generally very inhibiting for localsearch algorithms. A neutrality-based iterated local search, that allowsneutral walks to move on the plateaus, is proposed and experimented on apermutation flowshop scheduling problem with the aim of minimizing themakespan. Our experiments show that the proposed approach is able to findimproving solutions compared with a classical iterated local search. Moreover,the tradeoff between the exploitation of neutrality and the exploration of newparts of the search space is deeply analyzed.

NILS: a Neutrality-based Iterated Local Search and its application to Flowshop Scheduling