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This paper introduces an enhanced meta-heuristic (ML-ACO) that combinesmachine learning (ML) and ant colony optimization (ACO) to solve combinatorialoptimization problems. To illustrate the underlying mechanism of our ML-ACOalgorithm, we start by describing a test problem, the orienteering problem. Inthis problem, the objective is to find a route that visits a subset of verticesin a graph within a time budget to maximize the collected score. In the firstphase of our ML-ACO algorithm, an ML model is trained using a set of smallproblem instances where the optimal solution is known. Specifically,classification models are used to classify an edge as being part of the optimalroute, or not, using problem-specific features and statistical measures. Thetrained model is then used to predict the probability that an edge in the graphof a test problem instance belongs to the corresponding optimal route. In thesecond phase, we incorporate the predicted probabilities into the ACO componentof our algorithm, i.e., using the probability values as heuristic weights or towarm start the pheromone matrix. Here, the probability values bias samplingtowards favoring those predicted high-quality edges when constructing feasibleroutes. We have tested multiple classification models including graph neuralnetworks, logistic regression and support vector machines, and the experimentalresults show that our solution prediction approach consistently boosts theperformance of ACO. Further, we empirically show that our ML model trained onsmall synthetic instances generalizes well to large synthetic and real-worldinstances. Our approach integrating ML with a meta-heuristic is generic and canbe applied to a wide range of optimization problems.

Boosting Ant Colony Optimization via Solution Prediction and Machine Learning