Associate Professor Tsung-Che Chiang
Department of Computer Science and Information Engineering
National Taiwan Normal University

Tel: +886-2-77346692    Fax: +886-2-29322378    Email
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Thammarsat Visutarrom, Tsung-Che Chiang, Abdullah Konak, and Sadan Kulturel-Konak, Reinforcement learning-based differential evolution for solving economic dispatch problems, Proc. of IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 913 - 917, Singapore (virtual conference due to COVID-19),  Dec. 14-17, 2020. [Outstanding Paper Award]

Abstract

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.               

 

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