An Artificial Bee Colony Algorithm with Dual Groups and Multiple Strategies Based on Reinforcement Learning

The Artificial Bee Colony (ABC) algorithm is widely used for continuous optimization, but the standard ABC still suffers from insufficient use of neighborhood information, limited adaptability of search behavior, and random restarts in the scout bee phase, which may lead to slow convergence and reduced solution quality on complex problems. To address these limitations, this paper proposes an artificial bee colony algorithm with dual groups and multiple strategies based on reinforcement learning, named RLDMS-ABC. In the employed bee phase, Q-learning is used to adaptively adjust the neighborhood size of each food source according to search feedback, and the best individual selected from the sampled neighborhood guides candidate solution generation. In the onlooker bee phase, selected food sources are divided into elite and ordinary groups according to relative quality, and different search strategies are assigned to balance exploration and exploitation. In the scout bee phase, a guided restart mechanism combining opposition-based learning and the current global best solution is designed to help stagnated individuals escape local optima. Experiments on the CEC 2014 benchmark set show that RLDMS-ABC outperforms several representative ABC variants on most functions in terms of solution quality, convergence speed, and robustness.