- Queued Pareto Local Search for Multi-Objective Optimization
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
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- Proceedings title: Parallel problem solving from nature - PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September
13-17, 2014: proceedings
Place of publication: Cham
Editors: T. Bartz-Beielstein, J. Branke, B. Filipič, J. Smith
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