Adaptive Multi-objective Local Search Algorithms for the Permutation Flowshop Scheduling Problem

Automatic algorithm configuration (AAC) is an increasingly critical factor in the design of efficient metaheuristics. AAC was previously successfully applied to multi-objective local search (MOLS) algorithms using offline tools. However, offline approaches are usually very expensive, draw general recommendations regarding algorithm design for a given set of instances, and does generally not allow per-instance adaptation. Online techniques for automatic algorithm control are usually applied to single-objective evolutionary algorithms. In this work we investigate the impact of including control mechanisms to MOLS algorithms on a classical bi-objective permutation flowshop scheduling problem (PFSP), and demonstrate how even simple control mechanisms can complement traditional offline configuration techniques.