Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO) with Aphids, modelled after a real-world symbiotic relationship between ants and aphids. ACO with Aphids strategy is designed to improve solution quality of discrete domain Dynamic Optimization Problems (DOPs) with event-triggered discrete dynamism. The proposed strategy aims to improve the inter-state convergence rate throughout the entire dynamic optimization. It does so by minimizing the fitness penalty and maximizing the convergence speed that occurs after the dynamic change. This strategy is tested against Full-Restart and Pheromone-Sharing strategies implemented on the same ACO core algorithm solving Dynamic Multidimensional Knapsack Problem (DMKP) benchmarks. ACO with Aphids has demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2%. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5%.

翻译：目前可用的动态优化策略在蚁群优化（ACO）算法中面临两种权衡：算法收敛速度较慢，或每次动态变化后解质量显著下降。本文提出一种名为“基于蚜虫的蚁群优化（ACO with Aphids）”的离散动态优化策略，该策略模拟了自然界中蚂蚁与蚜虫的共生关系。该策略旨在提升具有事件触发离散动态特性的离散域动态优化问题（DOPs）的解质量，并在整个动态优化过程中提高状态间收敛速率。其核心机制是：最小化动态变化后的适应度惩罚，同时最大化收敛速度。我们将该策略与在相同ACO核心算法上实现的全重启（Full-Restart）策略和费洛蒙共享（Pheromone-Sharing）策略进行对比，以解决动态多维背包问题（DMKP）基准测试。实验结果表明，基于蚜虫的ACO在所有测试中均优于费洛蒙共享策略，平均差距降低29.2%。此外，对于大型数据集组，该策略优于全重启策略，总体平均差距降低52.5%。