Abstract
Decomposition-based algorithms seem promising for many-objective optimization problems. However, the issue of selecting a set of weighting vectors for more than two objectives is still unresolved and ad-hoc methods are predominantly used. In the present work, a novel concept is introduced which we call generalized decomposition. Generalized decomposition enables the analyst to adapt the generated distribution of Pareto optimal points, according to the preferences of the decision maker. Also it is shown that generalized decomposition unifies the three performance objectives in multi-objective optimization algorithms to only one, that of convergence to the Pareto front.
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Giagkiozis, I., Purshouse, R.C., Fleming, P.J. (2013). Generalized Decomposition. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_33
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DOI: https://doi.org/10.1007/978-3-642-37140-0_33
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