@inproceedings{70fd02d1d4f546f5900bcd7ebe4dbbd7,
title = "An Improved Fire Hawks Optimizer for Function Optimization",
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.",
keywords = "FHO, Fire Hawk Optimizer, H-FHO, Quasi-Random Sequence, SO-FHO, Swarm Intelligence, TO-FHO",
author = "Adnan Ashraf and Aliza Anwaar and \{Haider Bangyal\}, Waqas and Rabia Shakir and \{Ur Rehman\}, Najeeb and Zhao Qingjie",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 14th International Conference on Advances in Swarm Intelligence, ICSI 2023 ; Conference date: 14-07-2023 Through 18-07-2023",
year = "2023",
doi = "10.1007/978-3-031-36622-2\_6",
language = "English",
isbn = "9783031366215",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "68--79",
editor = "Ying Tan and Yuhui Shi and Wenjian Luo",
booktitle = "Advances in Swarm Intelligence - 14th International Conference, ICSI 2023, Proceedings",
address = "Germany",
}