New Initialisation Techniques for Multi-objective Local Search

Given the availability of high-performing local search (LS) for single-objective (SO) optimisation problems, a successful approach to tackle their multi-objective (MO) counterparts is scalarisation-based local search (SBLS). SBLS strategies solve multiple scalarisations, aggregations of the multiple objectives into a single scalar value, with varying weights. They have been shown to work specially well as the initialisation phase of other types of MO local search, e.g., Pareto local search (PLS). A drawback of existing SBLS strategies is that the underlying SO-LS method is unaware of the MO nature of the problem and returns only a single solution, discarding any intermediate solutions that may be of interest. We propose here two new SBLS initialisation strategies (ChangeRestart and ChangeDirection) that overcome this drawback by augmenting the underlying SO-LS method with an archive of nondominated solutions used to dynamically update the scalarisations. The new strategies produce better results on the bi-objective permutation flowshop problem than other five SBLS strategies from the literature, not only on their own but also when used as the initialisation phase of PLS.