Enhancing Population-based Search with Active Inference

TL;DR

This paper integrates Active Inference with population-based metaheuristics like Ant Colony Optimization to improve solution quality for combinatorial problems such as TSP, demonstrating marginal performance gains with potential for broader applicability.

Contribution

It introduces a novel approach combining Active Inference with metaheuristics, enhancing anticipatory adaptation in population-based search algorithms.

Findings

Active Inference integration improves solution quality.

Marginal increase in computational cost observed.

Performance patterns relate to graph topology.

Abstract

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.