Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm.
Bibliographical noteThis work was supported in part by
the Engineering and Physical Sciences Research Council of U.K. under Grant
EP/K001310/1, in part by the National Natural Science Foundation of China
under Grant 61673331 and Grant 61673397, and in part by the EU Horizon
2020 Marie Sklodowska-Curie Individual Fellowships under Project 661327.
© 1997-2012 IEEE.
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- evolutionary algorithm
- multiobjective optimization
- scalarizing function (SF)