A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization
- Authors
- Gao, Meng; Liu, Xiao-Fang; Zhan, Zhi-Hui; Zhang, Jun
- Issue Date
- Dec-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- dynamic optimization; evolutionary computation; support vector machine; time-linkage
- Citation
- 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp 953 - 958
- Pages
- 6
- Indexed
- SCOPUS
- Journal Title
- 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
- Start Page
- 953
- End Page
- 958
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/120026
- DOI
- 10.1109/SSCI52147.2023.10371998
- Abstract
- Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time. © 2023 IEEE.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/120026)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.