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Queued Pareto Local Search for Multi-Objective Optimization
Maarten Inja, Chiel Kooijman, Maarten de Waard, Diederik M. Roijers, and Shimon Whiteson
Informatics Institute, University of Amsterdam, Amsterdam, The Netherlandsmaarten.inja@gmail.com
chiel.kooijman@gmail.com
mrtndwrd@gmail.com
d.m.roijers@uva.nl
s.a.whiteson@uva.nl
Abstract. 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), which 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 show that it can be embedded in a genetic search scheme that improves the approximate Pareto front with every iteration. We also show that QPLS produces good approximations faster, and leads to better approximations than popular alternative MOEAs.
LNCS 8672, p. 589 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014