Q-Learning-based parameter control in differential evolution for structural optimization

• Published in: Applied soft computing Vol. 107; p. 107464
• Main Authors: Huynh, Thanh N., Do, Dieu T.T., Lee, Jaehong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2021
• Subjects: Adaptive control parameter; Differential evolution; Optimization; Q-learning algorithm; Truss structures; Differential evolution; Adaptive control parameter; Q-learning algorithm; Truss structures; Optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107464

The operations of metaheuristic optimization algorithms depend heavily on the setting of control parameters. Therefore the addition of adaptive control parameter has been widely studied and shown to enhance the problem flexibility and overall performance of the algorithms. This paper proposes Q-learning Differential Evolution (qlDE) algorithm, an adaptive control parameter Differential Evolution(DE) algorithm, for structural optimization. The reinforcement learning Q-learning model is integrated into DE as an adaptive parameter controller, adaptively adjusting control parameters of the algorithm at each search iteration in order to optimally regulate its behavior for different search domains. Moreover, by automatically controlling the balance of exploration and exploitation at different stages of the process, the performance of the optimizer will also be enhanced. To verify the effectiveness and robustness of the proposed qlDE algorithm in comparison with the classical DE and several other algorithms in the literature, five benchmark examples of truss structural weight minimizations will be performed in this study. •An adaptive control parameter Differential Evolution (DE) algorithm using Q-learning, called Qlearning DE (qlDE), is suggested for truss optimization.•The implementation of Q-learning as the parameter controller into DE is performed.•Five benchmark examples of truss optimization with multiple frequency constraints are tested to verify the effectiveness and robustness of the proposed algorithm.•The qlDE significantly improves the convergence speed while still ensuring solution accuracy.