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@incollection{yang2010new,
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}

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@article{Rashedi2009,
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@article{Khajehzadeh2012,
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}

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author = {Kennedy, James and Eberhart, Russell C.},
title = {Swarm Intelligence},
year = {2001},
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address = {San Francisco, CA, USA}
}

@article{Mirjalili2014,
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@article{MIRJALILI201651,
  title = {The Whale Optimization Algorithm},
  journal = {Advances in Engineering Software},
  volume = {95},
  pages = {51-67},
  year = {2016},
  issn = {0965-9978},
  doi = {https://doi.org/10.1016/j.advengsoft.2016.01.008},
  url = {https://www.sciencedirect.com/science/article/pii/S0965997816300163},
  author = {Seyedali Mirjalili and Andrew Lewis},
  keywords = {Optimization, Benchmark, Constrained optimization, Particle swarm optimization, Algorithm, Heuristic algorithm, Genetic algorithm, Structural optimization},
  abstract = {This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional  methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html}
}

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}

@article{Mirjalili2015,
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  title = {Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm},
  journal = {Knowledge-Based Systems}
}

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@Book{rao2019engineering,
 author = {Rao, Singiresu},
 title = {Engineering optimization : theory and practice},
 publisher = {John Wiley and Sons, Ltd},
 year = {2019},
 address = {Hoboken, NJ, USA},
 isbn = {978-1-119-45479-3}
 }

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