Abstract
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.
Original language | English |
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Pages (from-to) | 296-313 |
Number of pages | 18 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 22 |
Issue number | 2 |
Early online date | 29 Jul 2017 |
DOIs | |
Publication status | Published - Apr 2018 |
Bibliographical note
This work was supported in part bythe 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.
Publisher Copyright:
© 1997-2012 IEEE.
Data Availability Statement
This paper has supplementary downloadable multimedia material availableat http://ieeexplore.ieee.org provided by the authors.
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Keywords
- Decomposition
- evolutionary algorithm
- multiobjective optimization
- scalarizing function (SF)