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An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The Artificial Bee Colony (ABC) algorithm is a swarm-based optimization technique inspired by honeybee foraging.
  • Global optimization problems require robust algorithms that balance exploration and exploitation.

Purpose of the Study:

  • To propose a new variant of the ABC algorithm, termed ABC-SA, to enhance performance on global optimization problems.
  • To improve the intensification and diversification capabilities of the ABC algorithm.

Main Methods:

  • Introduced a novel solution acceptance rule allowing probabilistic acceptance of worse candidate solutions.
  • Implemented a probabilistic multisearch strategy using three distinct search equations with adaptive probabilities.
  • Tested the proposed ABC-SA algorithm on benchmark functions across various dimensions.

Main Results:

  • The ABC-SA algorithm demonstrated improved intensification and diversification compared to the standard ABC algorithm.
  • Computational results indicated that ABC-SA outperformed other ABC variants.
  • The proposed ABC-SA showed superiority over several recent state-of-the-art optimization algorithms.

Conclusions:

  • The enhanced solution acceptance rule and probabilistic multisearch strategy significantly improve ABC algorithm performance.
  • ABC-SA is an effective and superior approach for addressing global optimization challenges.
  • The findings suggest ABC-SA as a competitive alternative to existing advanced optimization techniques.