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This study introduces a novel service composition method, fusing beetle-ant colony optimization (Be-ACO), to enhance big data environment solutions. The Be-ACO algorithm improves optimization accuracy and speed for complex user needs.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Big Data Analytics

Background:

  • Web service composition is challenging in big data due to complex user needs and service uncertainty.
  • Existing ant colony optimization (ACO) methods face limitations in solution accuracy and speed for constrained optimization models.

Purpose of the Study:

  • To improve the accuracy and computing speed of constrained optimization models for web service composition.
  • To propose a novel service composition method that overcomes local optimization issues.

Main Methods:

  • Introduced the beetle antenna search (BAS) strategy to mitigate local optimization risks.
  • Developed a fused beetle-ant colony optimization algorithm (Be-ACO) for service composition.
  • Implemented a method where BAS generates search subspaces for ACO traversal, leading to global optimal solutions.

Main Results:

  • The Be-ACO method significantly improves convergence performance compared to traditional optimization methods.
  • Experimental results demonstrate a substantial increase in solution accuracy.
  • The fused algorithm effectively navigates complex search spaces for optimized service composition.

Conclusions:

  • The Be-ACO algorithm offers a robust solution for complex web service composition in big data environments.
  • This approach enhances both the speed and accuracy of finding optimal service combinations.
  • The integration of BAS with ACO provides a powerful tool for addressing optimization challenges.