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Area of Science:

  • Swarm Intelligence and Optimization Algorithms
  • Computational Intelligence
  • Metaheuristic Optimization

Background:

  • Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm inspired by bat echolocation.
  • BA exhibits characteristics like ease of use, flexibility, adaptability, and consistency.
  • The algorithm incorporates natural selection principles for optimization.

Purpose of the Study:

  • To review and analyze state-of-the-art research utilizing the Bat-inspired Algorithm (BA) from 2017 to 2021.
  • To summarize the growth, different versions, and applications of BA.
  • To critically analyze BA's limitations and suggest future research directions.

Main Methods:

  • Systematic literature review of Scopus-indexed articles published between 2017 and 2021.
  • Analysis of publication trends, including yearly publications, citations, top authors, institutions, and countries.
  • Categorization and summary of various BA versions (binary, modified, hybridized, multiobjective) and their applications.

Main Results:

  • Significant growth in BA-based research from 2017-2021, with diverse applications across multiple engineering and scientific domains.
  • Identification of key research areas, including electrical systems, wireless networks, materials engineering, classification, robotics, and healthcare.
  • Summary of different BA variants and their suitability for complex optimization problems.

Conclusions:

  • Bat-inspired algorithm (BA) is a versatile and effective optimization tool with a growing body of research.
  • BA has demonstrated success in a wide array of complex problem domains.
  • Future research should focus on enhancing BA for dynamic, robust, and large-scale optimization, and improving its core mechanisms.