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A new Binary Pufferfish Optimization Algorithm (BPOA) effectively solves complex binary problems in Industry 4.0. Its performance hinges on carefully selecting transfer functions and binarization rules for optimal discretization.

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Area of Science:

  • Optimization algorithms
  • Computational intelligence
  • Industrial applications

Background:

  • Metaheuristics are crucial for Industry 4.0, enabling solutions for complex optimization problems.
  • Binary optimization problems, where variables are 0 or 1, are significant in modern industry.

Purpose of the Study:

  • To introduce a binary version of the Pufferfish optimization algorithm (BPOA).
  • To investigate the impact of different transfer functions and binarization rules on BPOA's performance.
  • To validate BPOA on real-world industrial binary problems.

Main Methods:

  • Developed a two-step binary mapping technique using transfer functions and binarization rules.
  • Compared BPOA against Particle Swarm Optimization, Secretary Bird Optimization Algorithm, and Arithmetic Optimization Algorithm.
  • Applied BPOA to Set Covering Problem, Unicost Set Covering Problem, and Knapsack Problem.

Main Results:

  • BPOA demonstrated promising and statistically validated results on industrial binary problems.
  • Performance was significantly influenced by the choice of transfer function-binarization rule pairings.
  • Statistical significance in performance differences emerged when solution quality diverged.

Conclusions:

  • BPOA is a competitive and flexible framework for binary optimization.
  • The effectiveness of BPOA is primarily determined by its discretization strategy (transfer-rule pairing).
  • Further research into discretization design can enhance BPOA's capabilities.