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<p>The Firefly Algorithm (FA), while effective for complex optimization, suffers from inherent limitations such as search oscillation and low convergence precision. To address these issues, a firefly algorithm with multiple learning ability based on gender difference (MLFA-GD) is proposed. Firstly, the algorithm evenly divides the randomly initialized population into male and female subgroups. Then a male firefly learning strategy which incorporated a partial attraction model combining with an escape mechanism, and a female firefly learning strategy guided by both the generalized centroid of the male subgroup and the global optimal individual are designed separately. Additionally, a random walk strategy is further incorporated to refine the optimization accuracy. Different from existing gender-based FA variants, male fireflies either fly toward brighter female fireflies or move away from weaker individuals to enhance exploration capability. Meanwhile, female fireflies update positions guided by two elite male individuals, effectively leveraging historical search information to improve exploitation capability. The performance is evaluated on 23 numerical functions, 30 CEC 2017 benchmark functions and an automatic test data generation problem. The experiment comparison results with six FA variants and ten popular meta heuristic algorithms confirm its enhanced search capability and significantly higher optimization precision, validating its effectiveness in balancing exploration and exploitation.</p>

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Firefly algorithm with multiple learning ability based on gender difference

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Wenning Zhang,
  • Chongyang Jiao,
  • Qinglei Zhou

摘要

The Firefly Algorithm (FA), while effective for complex optimization, suffers from inherent limitations such as search oscillation and low convergence precision. To address these issues, a firefly algorithm with multiple learning ability based on gender difference (MLFA-GD) is proposed. Firstly, the algorithm evenly divides the randomly initialized population into male and female subgroups. Then a male firefly learning strategy which incorporated a partial attraction model combining with an escape mechanism, and a female firefly learning strategy guided by both the generalized centroid of the male subgroup and the global optimal individual are designed separately. Additionally, a random walk strategy is further incorporated to refine the optimization accuracy. Different from existing gender-based FA variants, male fireflies either fly toward brighter female fireflies or move away from weaker individuals to enhance exploration capability. Meanwhile, female fireflies update positions guided by two elite male individuals, effectively leveraging historical search information to improve exploitation capability. The performance is evaluated on 23 numerical functions, 30 CEC 2017 benchmark functions and an automatic test data generation problem. The experiment comparison results with six FA variants and ten popular meta heuristic algorithms confirm its enhanced search capability and significantly higher optimization precision, validating its effectiveness in balancing exploration and exploitation.