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A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems.

José García1, Paola Moraga1, Matias Valenzuela1

  • 1Pontificia Universidad Católica de Valparíso, 2362807 Valparaíso, Chile.

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Summary

This study integrates unsupervised machine learning (db-scan) with metaheuristic algorithms for combinatorial optimization problems. The novel approach enhances solution quality and speeds up convergence compared to existing binarization methods.

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Machine Learning

Background:

  • Hybrid techniques combining machine learning and metaheuristic algorithms show promise for solving complex optimization problems.
  • Improving solution quality and reducing convergence times are key challenges in operations research for combinatorial optimization problems (COPs).

Purpose of the Study:

  • To explore the integration of the db-scan unsupervised learning technique into the binarization process of continuous swarm intelligence metaheuristic algorithms.
  • To systematically analyze the contribution of the db-scan operator in binarization and compare its performance against other binarization methods.

Main Methods:

  • The study employs the db-scan unsupervised learning technique for the binarization of continuous metaheuristic algorithms.
  • Performance is evaluated by comparing db-scan with random operators, clustering-based methods, and transfer functions (TFs) on the set covering problem and a real-world problem.

Main Results:

  • The integration of db-scan consistently yielded superior results in computation time and solution quality compared to TFs and random operators.
  • When compared with other clustering techniques, db-scan demonstrated significantly improved convergence times.

Conclusions:

  • The db-scan technique offers a robust and efficient method for binarizing continuous metaheuristic algorithms in COPs.
  • This approach represents a valuable advancement for operations research, enhancing both the speed and accuracy of optimization solutions.