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A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems.

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This study introduces a novel Chaotic and Neighborhood Search-based ABC algorithm (CNSABC) to improve upon the traditional Artificial Bee Colony (ABC) algorithm. CNSABC enhances convergence and search capabilities for optimization problems.

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Area of Science:

  • Artificial Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • Traditional Artificial Bee Colony (ABC) algorithms suffer from under-exploitation and slow convergence.
  • Developing improved heuristic algorithms is crucial for advancing artificial intelligence research.

Purpose of the Study:

  • To propose a novel variant of the ABC algorithm, named Chaotic and Neighborhood Search-based ABC algorithm (CNSABC).
  • To address the shortcomings of the traditional ABC algorithm, specifically under-exploitation and slow convergence.

Main Methods:

  • Introduced Bernoulli chaotic mapping with a mutual exclusion mechanism to enhance diversity and exploration.
  • Incorporated a neighborhood search mechanism with a compression factor for improved convergence and exploitation.
  • Integrated sustained bees to further enhance the algorithm's performance.

Main Results:

  • Experimental results demonstrate that the proposed CNSABC exhibits superior convergence efficiency and search ability compared to traditional methods.
  • The three improved mechanisms individually contribute to the overall effectiveness of CNSABC.
  • CNSABC successfully applied to engineering optimization problems, yielding satisfactory solutions.

Conclusions:

  • The novel CNSABC algorithm effectively overcomes the limitations of the traditional ABC algorithm.
  • CNSABC offers enhanced exploration, exploitation, and convergence efficiency for optimization tasks.
  • The algorithm shows practical applicability in solving complex engineering optimization problems.