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The butterfly optimization algorithm (BOA), a swarm-based metaheuristic, is reviewed for its effectiveness in solving diverse optimization problems. This summary covers its adaptations, applications, and future research directions.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The butterfly optimization algorithm (BOA) is a nature-inspired metaheuristic algorithm.
  • BOA is recognized for its simplicity, few adaptive parameters, and effective balance between exploration and exploitation.

Purpose of the Study:

  • To comprehensively review and summarize recent studies employing the BOA for various optimization challenges.
  • To provide an overview of BOA's foundational concepts, mathematical model, and capabilities.

Main Methods:

  • Literature review of recently published studies utilizing the BOA.
  • Classification of BOA adaptations into original, modified, and hybridized forms.
  • Analysis of BOA's advantages and disadvantages in optimization.

Main Results:

  • The BOA has been widely adapted and applied across numerous domains due to its performance.
  • Studies are categorized based on their approach to modifying or hybridizing the BOA.
  • Key applications and performance characteristics of the BOA are detailed.

Conclusions:

  • The BOA is a versatile and powerful optimization tool with significant potential.
  • Further research can explore novel applications and enhancements of the BOA.
  • This review serves as a valuable resource for researchers interested in the BOA.