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Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional

Jia Guo1,2, Guoyuan Zhou3, Ke Yan4

  • 1School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.

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|October 25, 2023
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Summary
This summary is machine-generated.

This study introduces the Pair Barracuda Swarm Optimization (PBSO) algorithm to overcome local optima and dimensional catastrophes in high-dimensional optimization. PBSO demonstrates superior performance compared to state-of-the-art methods on benchmark functions.

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Area of Science:

  • Intelligent Computing
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • High-dimensional optimization poses significant challenges for traditional evolutionary algorithms.
  • Existing methods often suffer from dimensional catastrophes and local optima, reducing accuracy.
  • Novel approaches are required to effectively navigate complex, high-dimensional search spaces.

Purpose of the Study:

  • Introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm.
  • Address the limitations of traditional methods in high-dimensional optimization.
  • Enhance global search capabilities and mitigate local optima entrapment.

Main Methods:

  • Developed the Pair Barracuda Swarm Optimization (PBSO) algorithm with a unique barracuda pairing strategy.
  • Incorporated a support barracuda to augment the leading pair and improve global search.
  • Evaluated PBSO performance using the CEC2017 standard test functions.

Main Results:

  • PBSO achieved superior performance across 29 test functions compared to five state-of-the-art optimizers.
  • Secured 9 first-place, 13 second-place, and 5 third-place finishes, with an average rank of 2.0345.
  • Demonstrated effectiveness in mitigating dimensional catastrophes and local optima.

Conclusions:

  • The Pair Barracuda Swarm Optimization (PBSO) algorithm is a highly effective solution for high-dimensional optimization problems.
  • PBSO offers a dependable and superior alternative to existing natural-inspired optimizers.
  • The novel pairing strategy and support barracuda enhance robustness and accuracy in complex search spaces.