  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%.
