A machine learning framework for neighbor generation in metaheuristic search

TL;DR

This paper introduces a machine learning framework to enhance neighbor generation in metaheuristics, improving solution quality and search efficiency for combinatorial optimization problems.

Contribution

It proposes a novel ML-based neighbor generation method that learns variable selection strategies to guide metaheuristic search processes.

Findings

Improved solution quality in Wireless Network Optimization

Enhanced exploration and exploitation balance in Mixed-Integer Programs

Effective integration of ML with metaheuristics demonstrated

Abstract

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-off between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.