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Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization.

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

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Autonomous metaheuristic algorithms dynamically adjust parameters for complex optimization problems. These enhanced methods, including Autonomous Particle Swarm Optimization, show improved performance in high-dimensional search spaces.

Keywords:
CEC benchmarkautonomous algorithmsbat algorithmcontinuous populationcuckoo search algorithmhigh-density functionsmetaheuristicsoptimizationparticle swarm optimizationperformance comparison

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Machine Learning

Background:

  • Complex and high-dimensional optimization problems present significant challenges.
  • Metaheuristic algorithms offer potential solutions, with autonomous variants showing promise.
  • Autonomous algorithms dynamically adjust parameters based on performance without external input.

Purpose of the Study:

  • To leverage unsupervised machine learning clustering for autonomous population parameter configuration in metaheuristics.
  • To enhance metaheuristic intensification and diversification through search space clustering.
  • To imbue metaheuristics with adaptive capabilities for broader solution searching.

Main Methods:

  • Examination of autonomous metaheuristic algorithms: Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm.
  • Evaluation against original counterparts using high-density functions from the CEC LSGO benchmark suite.
  • Incorporation of unsupervised machine learning clustering for dynamic population adjustments.

Main Results:

  • Autonomous versions demonstrated performance enhancements over traditional counterparts.
  • Autonomous Particle Swarm Optimization consistently achieved superior optimal minimum values.
  • Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm showed significant advancements.

Conclusions:

  • Autonomous metaheuristics, particularly with continuous populations, effectively navigate complex, high-dimensional search spaces.
  • The intrinsic adaptability and autonomous decision-making of these algorithms represent a new era for optimization tools.
  • Further research and adaptation are recommended to fully realize the potential of autonomous algorithms in diverse applications.