NILS: a Neutrality-based Iterated Local Search and its application to Flowshop Scheduling
This addresses a specific bottleneck in local search algorithms for combinatorial optimization problems like flowshop scheduling, but it is incremental as it builds on existing iterated local search methods.
The paper tackled the challenge of neutrality in combinatorial optimization, where many solutions share the same fitness value, by proposing a neutrality-based iterated local search (NILS) for flowshop scheduling to minimize makespan, and found it improved solutions compared to a classical iterated local search.
This paper presents a new methodology that exploits specific characteristics from the fitness landscape. In particular, we are interested in the property of neutrality, that deals with the fact that the same fitness value is assigned to numerous solutions from the search space. Many combinatorial optimization problems share this property, that is generally very inhibiting for local search algorithms. A neutrality-based iterated local search, that allows neutral walks to move on the plateaus, is proposed and experimented on a permutation flowshop scheduling problem with the aim of minimizing the makespan. Our experiments show that the proposed approach is able to find improving solutions compared with a classical iterated local search. Moreover, the tradeoff between the exploitation of neutrality and the exploration of new parts of the search space is deeply analyzed.