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A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm.

Dante Leiva1, Benjamín Ramos-Tapia1, Broderick Crawford1

  • 1Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile.

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Summary
This summary is machine-generated.

This study introduces the Binary Growth Optimizer, a novel metaheuristic for the complex set-covering problem. It offers efficient and competitive solutions, outperforming traditional methods in speed and quality.

Keywords:
combinatorial problemsmetaheuristicsoptimizationset-covering problem

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Area of Science:

  • Computer Science
  • Operations Research
  • Artificial Intelligence

Background:

  • The set-covering problem is computationally complex, challenging traditional integer programming methods for large instances.
  • Metaheuristics offer effective approaches for solving complex optimization problems like set covering.
  • Existing metaheuristics show promise, but novel algorithms are needed for improved efficiency.

Purpose of the Study:

  • To introduce and analyze a novel metaheuristic for the set-covering problem.
  • To adapt the Growth Optimizer algorithm for binary optimization tasks.
  • To evaluate the performance of the proposed Binary Growth Optimizer algorithm.

Main Methods:

  • Developing a Binary Growth Optimizer algorithm inspired by human behavior and the continuous Growth Optimizer.
  • Implementing the Binary Growth Optimizer for solving set-covering instances.
  • Analyzing the algorithm's performance in terms of resolution time and solution quality.

Main Results:

  • The Binary Growth Optimizer demonstrates capability in achieving competitive and efficient solutions.
  • Experimental results show effectiveness compared to other optimization strategies.
  • The algorithm balances resolution time and result quality effectively.

Conclusions:

  • The Binary Growth Optimizer is a viable and efficient metaheuristic for the set-covering problem.
  • The approach shows promise for complex binary optimization problems.
  • Further research can explore its application in diverse real-world scenarios.