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A neural knowledge learning-driven artificial bee colony algorithm with reinforcement adaptation for global optimization
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AI
DOI：10.1016/j.asoc.2026.114662.png)
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Abstract
En 中文
Evolutionary algorithms often suffer from search inefficiency due to their inability to systematically reuse histor ical search patterns, leading to redundant exploration and premature stagnation. Addressing this limitation, propose KLABC-RL, a novel framework that synergizes Reinforcement Learning (RL) with Knowledge Learning Evolutionary Computation (KLEC) within the Artificial Bee Colony (ABC) paradigm. Unlike conventional hybrids that enforce static knowledge transfer, KLABC-RL employs a Q-learning-based adaptive agent to dynamically govern the search process. This agent intelligently toggles between an Artificial Neural Network (ANN)-driven Knowledge Learning Model (KLM) for exploitation and standard ABC operators for exploration, thereby effec tively preventing negative knowledge transfer. To further mitigate stagnation, a Hilbert space-based perturbation strategy is integrated into the scout phase, enhancing population diversity. Comprehensive evaluations on 23 clas sical benchmark functions, the IEEE CEC 2019 suite, and complex real-world engineering problems, specifically planar kinematic arm control and photovoltaic (PV) parameter extraction demonstrate the superiority of KLABC-RL. Comparative analysis against seven state-of-the-art algorithms and 4 hybrid variants of ABC reveals that KLABC-RL achieves significantly faster convergence and higher solution accuracy. Rigorous statistical validation, including Wilcoxon Rank-Sum, Friedman, and ANOVA tests, confirms the robustness and efficacy of the proposed framework in advancing intelligent evolutionary search.
Keywords:
Evolutionary computation
Swarm intelligence
Artificial bee colony algorithm
Transfer learning
Reinforcement learning
Hilbert space
Evolutionary transfer optimization
Journal
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6.6
Papers:
1.4W
Citations:
4.8W
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