Queued Pareto Local Search for Multi−Objective Optimization
Maarten Inja‚ Chiel Kooijman‚ Maarten de Waard‚ Diederik Roijers and Shimon Whiteson
Many real-world optimization problems involve balancing multiple objectives. When there is no solution that is best with respect to all objectives, it is often desirable to compute the Pareto front. This paper proposes queued Pareto local search (QPLS). QPLS improves on existing Pareto local search (PLS) methods by maintaining a queue of improvements preventing premature exclusion of dominated solutions. We prove that QPLS terminates and that, when embedded in a genetic search scheme, it is guaranteed to converge to the true Pareto front. We show that QPLS produces good approximations faster, and leads to better approximations than popular alternative MOEAs.