Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this article introduces targeted modification to the certain values in the bottleneck dimensions. An...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2023-08, Vol.27 (4), p.964-979
Main Authors: Wang, Zi-Jia, Jian, Jun-Rong, Zhan, Zhi-Hui, Li, Yun, Kwong, Sam, Zhang, Jun
Format: Article
Language:English
Subjects:
Algorithms
Computer science
Differential evolution (DE)
Evolutionary computation
gene targeting (GT)
large-scale optimization
Nobel Prize
Optimization
Probabilistic logic
simple and efficient
Sociology
Stem cells
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Summary:Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this article introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in the candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3185665