Boosting Ant Colony Optimization via Solution Prediction and Machine Learning

07/29/2020
by   Yuan Sun, et al.
0

This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our enhanced algorithm, we start by describing a test problem – the orienteering problem – used to demonstrate the efficacy of ML-ACO. In this problem, the objective is to find a route that visits a subset of vertices in a graph within a time budget to maximize the collected score. In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known. Specifically, classification models are used to classify an edge as being part of the optimal route, or not, using problem-specific features and statistical measures. We have tested several classification models including graph neural networks, logistic regression and support vector machines. The trained model is then used to predict the probability that an edge in the graph of a test problem instance belongs to the corresponding optimal route. In the second phase, we incorporate the predicted probabilities into the ACO component of our algorithm. Here, the probability values bias sampling towards favoring those predicted high-quality edges when constructing feasible routes. We empirically show that ML-ACO generates results that are significantly better than the standard ACO algorithm, especially when the computational budget is limited. Furthermore, we show our algorithm is robust in the sense that (a) its overall performance is not sensitive to any particular classification model, and (b) it generalizes well to large and real-world problem instances. Our approach integrating ML with a meta-heuristic is generic and can be applied to a wide range of combinatorial optimization problems.

READ FULL TEXT
research
05/12/2020

Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems

Combinatorial optimization plays an important role in real-world problem...
research
08/17/2021

A New Constructive Heuristic driven by Machine Learning for the Traveling Salesman Problem

Recent systems applying Machine Learning (ML) to solve the Traveling Sal...
research
04/15/2020

Ants can orienteer a thief in their robbery

We address the Thief Orienteering Problem (ThOP), a multi-component prob...
research
07/04/2022

The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems

Set covering problem is an important class of combinatorial optimization...
research
10/04/2021

Pharmacoprint – a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design

Structural fingerprints and pharmacophore modeling are methodologies tha...
research
06/21/2021

Matrix Encoding Networks for Neural Combinatorial Optimization

Machine Learning (ML) can help solve combinatorial optimization (CO) pro...
research
12/04/2022

Characterizing instance hardness in classification and regression problems

Some recent pieces of work in the Machine Learning (ML) literature have ...

Please sign up or login with your details

Forgot password? Click here to reset