Abstract
The optimization of dynamic systems has been a complex research problem in various fields of study, especially in ecology and communications. In the field of ecology, the Lotka-Volterra model explains the interaction between predator and prey populations and provides mathematical equations of their dynamics. Traditional mathematical models are usually unable to accommodate the dynamic involvement, which calls for more flexible ideas. Ant Colony optimization, which is a metaheuristic weight-based algorithm, uses multiple iterations to solve tough optimization problems efficiently, where classical methods have failed. To assess the effectiveness of adaptive optimization in ecological systems, the proposed framework is tested on both synthetic predator–prey datasets and the classical Hudson Bay lynx–hare population data. Compared to traditional baselines, it showed improved trajectory fitting and stability characteristics. This paper demonstrates the potential of Dynamic Impact Ant Colony optimization to be able to stabilize and optimize Lotka-Volterra systems under dynamic conditions.