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The enhanced Adaptive Spiral Strategy Dung Beetle Optimization (ADBO) algorithm improves swarm intelligence for complex engineering problems. It offers faster, more efficient global exploration and superior performance in real-world applications.

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • The Dung Beetle Optimization (DBO) algorithm is a swarm intelligence technique for engineering design.
  • Existing DBO limitations include poor initialization, slow speeds, and limited global exploration.
  • These issues hinder its effectiveness in solving complex real-world problems.

Purpose of the Study:

  • To introduce an enhanced Dung Beetle Optimization algorithm, the Adaptive Spiral Strategy Dung Beetle Optimization (ADBO).
  • To address the limitations of the original DBO algorithm, improving its performance and applicability.
  • To enhance global exploration and search efficiency for complex optimization tasks.

Main Methods:

  • Implemented Gaussian Chaos strategy for superior population initialization.
  • Integrated Whale Spiral Search Strategy for enhanced search dynamics.
  • Introduced an adaptive weight factor to optimize search efficiency and global exploration.
  • Evaluated ADBO against benchmark algorithms using CEC2017 test functions.

Main Results:

  • ADBO demonstrated superior performance compared to existing benchmark algorithms.
  • The enhanced algorithm showed significant improvements in search speed and global exploration.
  • Validated effectiveness across diverse engineering applications, including robot manipulators and UAV path planning.

Conclusions:

  • The proposed ADBO algorithm effectively overcomes the limitations of the original DBO.
  • ADBO offers enhanced capabilities for solving complex engineering design challenges.
  • The algorithm shows significant potential for real-world applications, notably improving UAV safety and energy efficiency.