Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

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 the...

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Vydané v:IEEE transactions on evolutionary computation Ročník 22; číslo 2; s. 296 - 313
Hlavní autori: Jiang, Shouyong, Yang, Shengxiang, Wang, Yong, Liu, Xiaobin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2018
Predmet:
Algorithm design and analysis
Convergence
Decomposition
Electronic mail
evolutionary algorithm
Evolutionary computation
multiobjective optimization
Optimization
scalarizing function (SF)
Sociology
Statistics
ISSN:1089-778X, 1941-0026
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Popis
Shrnutí: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2707980