Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems

By Juliano Pierezan, Leandro dos S. Coelho. IEEE Congress on Evolutionary Computation, 2018

472 citations recorded by the source corpus.

The behavior of natural phenomena has become one of the most popular sources for researchers to design optimization algorithms for scientific, computing and engineering fields. As a result, a lot of nature-inspired algorithms have been proposed in the last decades. Due to the numerous issues of the global optimization process, new algorithms are always welcome in this research field. This paper introduces the Coyote Optimization Algorithm (COA), which is a population based metaheuristic for optimization inspired on the canis latrans species. It contributes with a new algorithmic structure and mechanisms for balancing exploration and exploitation. A set of boundary constrained real parameter optimization benchmarks is tested and a comparative study with other nature-inspired metaheuristics is provided to investigate the performance of the COA. Numerical results and non-parametric statistical significance tests indicate that the COA is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/a32f9a362f9a7495c85e2c80d115c9385eac1066

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In Artificial Intelligence

  1. Kusum Deep
  2. Swagatam Das
  3. Kanchan Rajwar
  4. Ponnuthurai Nagaratnam Suganthan
  5. Pinar Çivicioglu

In Computational Theory and Mathematics

  1. Kusum Deep
  2. Swagatam Das
  3. Kanchan Rajwar
  4. Ponnuthurai Nagaratnam Suganthan
  5. Pinar Çivicioglu

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