Abstract
This work proposes a hybrid strategy in a two-stage search process for many-objective optimization. The first stage of the search is directed by a scalarization function and the second one by Pareto selection enhanced with Adaptive ε-Ranking. The scalarization strategy drives the population towards central regions of objective space, aiming to find solutions with good convergence properties to seed the second stage of the search. Adaptive ε-Ranking balances the search effort towards the different regions of objective space to find solutions with good convergence, spread, and distribution properties. We test the proposed hybrid strategy on MNK-Landscapes showing that performance can improve significantly on problems with more than 6 objectives.
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Aguirre, H., Tanaka, K. (2010). A Hybrid Scalarization and Adaptive ε-Ranking Strategy for Many-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_2
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DOI: https://doi.org/10.1007/978-3-642-15871-1_2
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