Reinforcement learning-based differential evolution for solving economic dispatch problems

Thammarsat Visutarrom, Tsung Che Chiang, Abdullah Konak, Sadan Kulturel-Konak

研究成果: 書貢獻/報告類型會議論文篇章

摘要

In power systems, economic dispatch (ED) deals with the power allocation of power generation units to meet the power demand and minimize the cost. Many metaheuristics have been proposed to solve the ED problem with promising results. However, the performance of these algorithms might be sensitive to their parameter settings, and parameter tuning requires considerable effort. In this paper, a reinforcement learning (RL)-based differential evolution (DE) is proposed to solve the ED problem. We develop an RL mechanism to adaptively set two critical parameters, crossover rate (CR) and scaling factor (F), of DE. The performance of the proposed RLDE is compared with the canonical DE and several algorithms in the literature using three test systems. Our algorithm shows good solution quality and strong robustness.

原文英語
主出版物標題2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
發行者IEEE Computer Society
頁面913-917
頁數5
ISBN(電子)9781538672204
DOIs
出版狀態已發佈 - 2020 十二月 14
事件2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 - Virtual, Singapore, 新加坡
持續時間: 2020 十二月 142020 十二月 17

出版系列

名字IEEE International Conference on Industrial Engineering and Engineering Management
2020-December
ISSN(列印)2157-3611
ISSN(電子)2157-362X

會議

會議2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
國家新加坡
城市Virtual, Singapore
期間2020/12/142020/12/17

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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