A considerable amount of research has been devoted to solving static optimization problems via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all problem-related data remain unchanged during the optimization process, which is not a realistic assumption. Recently, dynamic optimization problems (DOPs) grabbed remarkable attention from the research community. However, the literature still lacks clear guidelines on selecting the most appropriate bio-inspired algorithm under changing environments. Due to the availability of many design choices, the selection of a suitable bio-inspired metaheuristic algorithm becomes an immediate challenge. This study proposes an algorithm recommendation architecture using Q-learning and hyper-heuristic approaches to help decision-makers select the most suitable bio-inspired algorithm for a given problem. To this end, Artificial Bee Colony (ABC), Manta Ray Foraging Optimization (MRFO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA) are employed as low-level optimizers so that the Q-learning and hyper-heuristic automatically select the optimizer in each cycle of the optimization process. The proposed algorithms are implemented in dynamic multidimensional knapsack problems, a natural extension of the well-known 0–1 knapsack problem. The performances of the recommender and standalone bio-inspired algorithms are evaluated through a comprehensive experimental analysis including appropriate statistical tests. Thus, the significant differences among the algorithms are revealed. The obtained results point out the efficiencies of the Q-learning-based algorithm recommender and MRFO in solving the dynamic multidimensional knapsack problem.

已经有大量研究致力于通过生物启发式元启发式算法来解决静态优化问题。但是，大多数算法都假设在优化过程中所有与问题相关的数据均保持不变，这是不现实的假设。最近，动态优化问题（DOP）引起了研究界的极大关注。但是，文献仍然缺乏在变化的环境中选择最合适的生物启发算法的明确指南。由于有许多设计选择可供选择，因此选择合适的生物启发式元启发式算法成为一项迫在眉睫的挑战。这项研究提出了一种使用Q学习和超启发式方法的算法推荐架构，以帮助决策者针对给定的问题选择最合适的生物启发算法。为此，采用人工蜂群（ABC），蝠For觅食优化（MRFO），蜂群算法（SSA）和鲸鱼优化算法（WOA）作为低级优化器，以便进行Q学习和超启发式在优化过程的每个周期中自动选择优化器。所提出的算法在动态多维背包问题中实现，这是众所周知的0–1背包问题的自然扩展。推荐器和独立生物启发算法的性能通过包括适当统计测试在内的综合实验分析进行评估。因此，揭示了算法之间的显着差异。获得的结果指出了基于Q学习的算法推荐器和MRFO在解决动态多维背包问题方面的效率。