A Variable Importance-Based Differential Evolution for Large-Scale Multiobjective Optimization

Large-scale multiobjective optimization problems (LMOPs) bring significant challenges for traditional evolutionary operators, as their search capability cannot efficiently handle the huge decision space. Some newly designed search methods for LMOPs usually classify all variables into different group...

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 12; s. 13048 - 13062
Hlavní autoři: Liu, Songbai, Lin, Qiuzhen, Tian, Ye, Tan, Kay Chen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Computer science
Convergence
Cybernetics
Differential evolution (DE)
Evolutionary algorithms
Evolutionary computation
large-scale optimization
Linear programming
multiobjective optimization
Multiple objective analysis
Optimization
Research and development
Search algorithms
Search methods
Search problems
variable grouping
Variables
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:Large-scale multiobjective optimization problems (LMOPs) bring significant challenges for traditional evolutionary operators, as their search capability cannot efficiently handle the huge decision space. Some newly designed search methods for LMOPs usually classify all variables into different groups and then optimize the variables in the same group with the same manner, which can speed up the population's convergence. Following this research direction, this article suggests a differential evolution (DE) algorithm that favors searching the variables with higher importance to the solving of LMOPs. The importance of each variable to the target LMOP is quantized and then all variables are categorized into different groups based on their importance. The variable groups with higher importance are allocated with more computational resources using DE. In this way, the proposed method can efficiently generate offspring in a low-dimensional search subspace formed by more important variables, which can significantly speed up the convergence. During the evolutionary process, this search subspace for DE will be expanded gradually, which can strike a good balance between exploration and exploitation in tackling LMOPs. Finally, the experiments validate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms for solving various benchmark LMOPs.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3098186