Enhancing Population-based Search with Active Inference

The Active Inference framework models perception and action as a unified process, where agents use probabilistic models to predict and actively minimize sensory discrepancies. In complement and contrast, traditional population-based metaheuristics rely on reactive environmental interactions without anticipatory adaptation. This paper proposes the integration of Active Inference into these metaheuristics to enhance performance through anticipatory environmental adaptation. We demonstrate this approach specifically with Ant Colony Optimization (ACO) on the Travelling Salesman Problem (TSP). Experimental results indicate that Active Inference can yield some improved solutions with only a marginal increase in computational cost, with interesting patterns of performance that relate to number and topology of nodes in the graph. Further work will characterize where and when different types of Active Inference augmentation of population metaheuristics may be efficacious.