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A new bats echolocation-based algorithm for single objective optimisation.

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Summary
This summary is machine-generated.

An adaptive bats sonar algorithm improves prey finding precision and speed by mimicking real bat echolocation. This enhanced swarm intelligence approach offers better accuracy and faster convergence for optimization problems.

Keywords:
Adaptive bats sonar algorithmBats echolocationBats sonar algorithmOptimisationReciprocal altruism

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • The Bats Sonar Algorithm (BSA) is a swarm intelligence method inspired by bat echolocation for prey finding.
  • Existing BSA lacks precision and convergence speed for optimal solutions.

Purpose of the Study:

  • Introduce an adaptive bats sonar algorithm (ABSA) incorporating real bat echolocation behaviors.
  • Enhance the precision and convergence rate of the BSA for optimization tasks.

Main Methods:

  • Developed an adaptive bats sonar algorithm (ABSA) based on novel paradigms of bat echolocation.
  • Validated ABSA performance using benchmark test functions for single-objective optimization.

Main Results:

  • The proposed adaptive bats sonar algorithm (ABSA) demonstrated superior performance compared to the standard BSA.
  • ABSA achieved higher accuracy and faster convergence rates in optimization tests.

Conclusions:

  • The adaptive bats sonar algorithm (ABSA) effectively addresses the limitations of the original BSA.
  • ABSA shows potential for efficient application in solving complex engineering optimization problems.