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

TL;DR

This paper introduces NILS, a neutrality-based iterated local search method that leverages fitness landscape neutrality to improve solutions in flowshop scheduling, outperforming classical methods.

Contribution

The paper proposes a novel neutrality-based iterated local search that effectively exploits landscape neutrality for better optimization in flowshop scheduling.

Findings

NILS outperforms classical iterated local search in finding better solutions.

Neutral walks on plateaus help explore the search space more effectively.

Tradeoff analysis between neutrality exploitation and exploration enhances understanding of search dynamics.

Abstract

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.