Improving the anytime behavior of two-phase local search

@article{DuboisLacoste2011ImprovingTA,
  title={Improving the anytime behavior of two-phase local search},
  author={J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle},
  journal={Annals of Mathematics and Artificial Intelligence},
  year={2011},
  volume={61},
  pages={125-154}
}
Algorithms based on the two-phase local search (TPLS) framework are a powerful method to efficiently tackle multi-objective combinatorial optimization problems. TPLS algorithms solve a sequence of scalarizations, that is, weighted sum aggregations, of the multi-objective problem. Each successive scalarization uses a different weight from a predefined sequence of weights. TPLS requires defining the stopping criterion (the number of weights) a priori, and it does not produce satisfactory results… CONTINUE READING

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