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<p>The Secretary Bird Optimisation Algorithm (SBOA) is a bio-inspired metaheuristic modelled on the hunting behaviour of the secretary bird. Recognised for its adaptability and robustness, SBOA has been effectively applied to complex optimisation problems across domains such as engineering, energy systems, healthcare, combinatorial optimisation, and artificial intelligence. Recent advancements include parameter-tuned, discrete, hybrid, and stochastic SBOA to enhance convergence speed, solution quality, and scalability. Despite its success, challenges such as premature convergence and parameter sensitivity persist, highlighting the need for further research into hybridisation and adaptive mechanisms. This paper comprehensively reviews SBOA’s developments, variants, applications, and emerging research directions, positioning SBOA as a promising tool for addressing diverse optimisation challenges.</p>

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Recent advances in secretary bird optimization algorithm, its variants and applications

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Yousef Sanjalawe,
  • Salam Al-E’mari,
  • Mosleh Abualhaj,
  • Sharif Naser Makhadmeh,
  • Mohammad A. Alsharaiah,
  • Duaa H. Hijazi

摘要

The Secretary Bird Optimisation Algorithm (SBOA) is a bio-inspired metaheuristic modelled on the hunting behaviour of the secretary bird. Recognised for its adaptability and robustness, SBOA has been effectively applied to complex optimisation problems across domains such as engineering, energy systems, healthcare, combinatorial optimisation, and artificial intelligence. Recent advancements include parameter-tuned, discrete, hybrid, and stochastic SBOA to enhance convergence speed, solution quality, and scalability. Despite its success, challenges such as premature convergence and parameter sensitivity persist, highlighting the need for further research into hybridisation and adaptive mechanisms. This paper comprehensively reviews SBOA’s developments, variants, applications, and emerging research directions, positioning SBOA as a promising tool for addressing diverse optimisation challenges.