Abstract
Two-Phase Local Search (TPLS) is a general algorithmic framework for multi-objective optimization. TPLS transforms a multi-objective problem into a sequence of single-objective ones by means of weighted sum aggregations. This paper studies different sequences of weights for defining the aggregated problems for the bi-objective case. In particular, we propose two weight setting strategies that show better anytime search characteristics than the original weight setting strategy used in the TPLS algorithm.
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Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T. (2010). Adaptive “Anytime” Two-Phase Local Search. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_5
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DOI: https://doi.org/10.1007/978-3-642-13800-3_5
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