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A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search.

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This study introduces a novel multistrategy artificial bee colony (ABCVNS) algorithm. ABCVNS enhances optimization performance by integrating variable neighborhood search strategies for improved exploration and exploitation balance.

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • The standard Artificial Bee Colony (ABC) algorithm exhibits a trade-off between exploration and exploitation capabilities.
  • Enhancing the comprehensive performance of ABC algorithms is crucial for complex optimization tasks.

Purpose of the Study:

  • To propose a novel multistrategy artificial bee colony algorithm (ABCVNS) that improves upon the standard ABC algorithm.
  • To enhance the balance between exploration and exploitation abilities within the ABC framework.

Main Methods:

  • Developed ABCVNS by incorporating Variable Neighborhood Search (VNS) principles.
  • Introduced two candidate search strategy pools for employed and onlooker bee phases (ABC/best/1, ABC/rand/1).
  • Integrated a random search strategy and opposition-based learning for the scout bee phase to balance exploration and exploitation.

Main Results:

  • Evaluated ABCVNS on two test suites comprising 58 problems.
  • Compared ABCVNS against several well-known optimization methods.
  • Experimental results demonstrated the effectiveness and superiority of the proposed ABCVNS algorithm.

Conclusions:

  • The proposed ABCVNS algorithm effectively balances exploration and exploitation.
  • ABCVNS shows significant improvements in performance compared to existing methods.
  • The integration of VNS strategies offers a promising direction for enhancing swarm intelligence algorithms.