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

Abstract

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 QLOBL_INFO 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.