An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms
Joint Authors
Sun, Qian
Yang, Jungang
Xu, Qingzheng
Fei, Rong
Wang, Na
Wang, Lei
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-08
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and several algorithms have been proposed in the literature.
Unfortunately, knowledge transfer between constituent tasks may cause negative effect on algorithm performance, especially when the optimal solutions of all tasks are in different locations of the unified search space.
To address this issue, an effective variable transformation strategy and the corresponding inverse transformation are proposed in multitasking optimization scenario.
After using variable transformation strategy, the estimated optimal solutions of all tasks are both near the center point of the unified search space.
More importantly, this strategy can enhance the task similarity, and then the effectiveness of knowledge transfer will probably be positive in this case, which can help us to improve the algorithm performance.
Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution.
In MTDE-VT, the individuals in the original population are first transformed into new locations by the variable transformation strategy.
Once the offspring is generated in the transformed unified search space, it must be transformed back to the original unified search space.
The statistical analysis of experimental results on some multitasking optimization benchmark problems illustrates the superiority of the proposed MTDE-VT algorithm in terms of solution accuracy and robustness.
Furthermore, the basic principle and the good parameter combination are also provided based on massive simulated data.
American Psychological Association (APA)
Xu, Qingzheng& Wang, Lei& Yang, Jungang& Wang, Na& Fei, Rong& Sun, Qian. 2020. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
Modern Language Association (MLA)
Xu, Qingzheng…[et al.]. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
American Medical Association (AMA)
Xu, Qingzheng& Wang, Lei& Yang, Jungang& Wang, Na& Fei, Rong& Sun, Qian. An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms. Complexity. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144615
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144615