Learning-aided Artificial Bee Colony with neural knowledge transfer for global optimization.

A critical challenge in swarm intelligence is the effective utilization of knowledge gained during the search, a process often confounded by the risk of negative knowledge transfer. To address this, we introduce the Learning-Aided Artificial Bee Colony (LA-ABC), a novel framework guided by a Neural Knowledge Transfer mechanism for global optimization. Our framework establishes a co-evolutionary mechanism between the search process of the ABC algorithm and an online neural knowledge learning engine. LA-ABC operates on a dual-pathway architecture, probabilistically arbitrating between foundational swarm exploration and a knowledge-transfer pathway. In this second pathway, an Artificial Neural Network (ANN) learns a predictive, non-linear model from a dynamic archive of historically successful solutions. This approach enables the model to interpret the complex context of successful moves, thereby preventing the negative knowledge transfer where a beneficial pattern in one region of the search space could be detrimental in another. This learned intelligence is then operationalized through a generative operator that transfers validated positive knowledge to create high-quality candidate solutions. The process transforms the ABC from a memoryless explorer into an intelligent agent that learns to navigate the fitness landscape with high efficacy. The superiority of the LA-ABC framework is demonstrated through comprehensive benchmarking on 23 standard test functions, the competitive IEEE CEC 2019 suite, and a real-world photovoltaic parameter extraction problem. Our proposed neural knowledge transfer approach significantly outperforms 12 state-of-the-art algorithms, including ABC, L-SHADE, JSO, L-DE, L-PSO, KL-variants, and RL variants with the significance of these improvements validated by rigorous statistical tests (Wilcoxon, Bonferroni-Dunn, Friedman, and ANOVA). Ultimately, LA-ABC provides a robust new paradigm for integrating reinforcement learning and knowledge transfer within evolutionary computation.