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New Binary Reptile Search Algorithms for Binary Optimization Problems.

Broderick Crawford1, Benjamín López Cortés1, Felipe Cisternas-Caneo1

  • 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.

Adapting continuous metaheuristics for binary optimization problems requires effective binarization. The Reptile Search Algorithm (RSA) shows competitive performance on NP-hard problems using a novel two-step binarization process.

Keywords:
binarization processbinary optimization problembinary rulesmetaheuristicsreptile search algorithmtransfer functions

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Computer Science

Background:

  • Binarizing continuous metaheuristics is crucial for solving NP-hard binary optimization problems.
  • Challenges include transforming continuous populations to binary solutions while maintaining search effectiveness.

Purpose of the Study:

  • To evaluate the performance of the Reptile Search Algorithm (RSA) on binary optimization problems.
  • To investigate a two-step binarization process using transfer functions and binarization rules.

Main Methods:

  • Applied the RSA to the Set Covering Problem and 0-1 Knapsack Problem.
  • Implemented a two-step binarization strategy combining transfer functions and binarization rules.
  • Conducted comparative experiments with Particle Swarm Optimization and Grey Wolf Optimizer.

Main Results:

  • The RSA demonstrated competitive performance on both benchmark problems, showcasing flexibility and adaptability.
  • The Z4 transfer function consistently improved performance across all tested algorithms.
  • Effectiveness of binarization strategies significantly impacts continuous metaheuristics' performance in binary domains.

Conclusions:

  • The RSA is a viable continuous metaheuristic for binary optimization tasks when appropriately binarized.
  • The Z4 transfer function shows promise as a robust component for binary optimization.
  • Careful selection of binarization strategies is essential for optimizing continuous metaheuristics for discrete problems.