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An Improved Fire Hawks Optimizer for Function Optimization

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Advances in Swarm Intelligence (ICSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13968))

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Abstract

Fire hawk Optimizer (FHO) is a relatively new intake in the family of evolutionary algorithms for a distinct type of optimization problem. Initialization of the population plays a significant role in solving classical optimization issues. Incorporating quasi-random sequences such as the sobol, halton, and torus sequences, this study demonstrates novel ways for swarm initiation. The outcomes of our proposed techniques display outstanding performance as compared with the traditional FHO. The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the standard approach. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.

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Author information

Authors and Affiliations

  1. Beijing Institute of Technology (BIT), Beijing, 100081, China

    Adnan Ashraf & Zhao Qingjie

  2. University of Sialkot (USKT), Sialkot, 51310, Pakistan

    Aliza Anwaar

  3. Kohsar University, Murree, 47150, Pakistan

    Waqas Haider Bangyal

  4. Department of Computer Science, FUUAST, Islamabad, Pakistan

    Rabia Shakir

  5. University of Gujrat, Gujrat, 50700, Pakistan

    Najeeb Ur Rehman

Authors
  1. Adnan Ashraf
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  2. Aliza Anwaar
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  3. Waqas Haider Bangyal
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  4. Rabia Shakir
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  5. Najeeb Ur Rehman
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  6. Zhao Qingjie
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Corresponding author

Correspondence to Adnan Ashraf .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Harbin Institute of Technology, Shenzhen, China

    Wenjian Luo

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Ashraf, A., Anwaar, A., Haider Bangyal, W., Shakir, R., Ur Rehman, N., Qingjie, Z. (2023). An Improved Fire Hawks Optimizer for Function Optimization. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_6

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  • DOI: https://doi.org/10.1007/978-3-031-36622-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36621-5

  • Online ISBN: 978-3-031-36622-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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