Reinforcement and opposition-based learning enhanced weighted mean of vectors algorithm for global optimization and feature selection

• Published in: Knowledge-based systems Vol. 319; p. 113626
• Main Authors: Gölcük, İlker, Ozsoydan, Fehmi Burcin, Durmaz, Esra Duygu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.06.2025
• Subjects: Opposition-based learning, Reinforcement learning, Global optimization, Feature selection; Weighted mean of vectors algorithm; Opposition-based learning, Reinforcement learning, Global optimization, Feature selection; Weighted mean of vectors algorithm
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2025.113626

•Q-learning guides adaptive selection of opposition-based learning variants in optimization.•Learning period mechanism enables automated opposition strategy identification.•A hybrid INFO algorithm called QLOBLINFO is proposed.•CEC2022 test suite and 30 real-world feature selection problems are solved.•Five constrained engineering design problems are effectively tackled. This paper presents a novel optimization algorithm that integrates reinforcement learning (RL) and opposition-based learning (OBL) mechanisms with the weighted mean of vectors algorithm (INFO). The OBL has proven effective in enhancing optimization algorithms, the lack of adaptive selection mechanisms often leads to suboptimal performance. The proliferation of OBL variants poses significant challenges in selecting appropriate mechanisms for specific optimization problems, as each variant exhibits distinct characteristics and performance patterns across different problem landscapes. This research addresses this limitation by introducing a novel RL framework for OBL selection. The proposed QLOBLINFO algorithm employs Q-learning to adaptively select among five OBL variants, enabling dynamic strategy adaptation during the optimization process. The algorithm's performance has been extensively evaluated using the CEC2022 benchmark suite, real-world feature selection problems, and constrained optimization problems. These results demonstrate that RL-based adaptive OBL selection represents an effective approach for enhancing optimization performance, particularly in complex optimization landscapes and real-world applications.