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An island parallel Harris hawks optimization algorithm

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Abstract

The Harris hawk optimization (HHO) is an impressive optimization algorithm that makes use of unique mathematical approaches. This study proposes an island parallel HHO (IP-HHO) version of the algorithm for optimizing continuous multi-dimensional problems for the first time in the literature. To evaluate the performance of the IP-HHO, thirteen unimodal and multimodal benchmark problems with different dimensions (30, 100, 500, and 1000) are evaluated. The implementation of this novel algorithm took into account the investigation, exploitation, and avoidance of local optima issues effectively. Parallel computation provides a multi-swarm environment for thousands of hawks simultaneously. On all issue cases, we were able to enhance the performance of the sequential version of the HHO algorithm. As the number of processors increases, the suggested IP-HHO method enhances its performance while retaining scalability and improving its computation speed. The IP-HHO method outperforms the other state-of-the-art metaheuristic algorithms on average as the size of the dimensions grows.

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Data Availability

The test functions and datasets that are used in this study are publicly available on website : (https://www.sfu.ca/ssurjano/optimization.html).

Notes

  1. https://user.ceng.metu.edu.tr/e1451970.

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Funding

This study is not funded by any institution.

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Author notes

  1. Tansel Dokeroglu and Ender Sevinc have contributed equally to this work.

Authors and Affiliations

  1. Software Engineering Department, Cankaya University, Etimesgut, Ankara, Turkey

    Tansel Dokeroglu

  2. Computer Engineering Department, Middle East Technical University, Ankara, Turkey

    Ender Sevinc

Authors
  1. Tansel Dokeroglu
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  2. Ender Sevinc
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Correspondence to Tansel Dokeroglu.

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Dokeroglu, T., Sevinc, E. An island parallel Harris hawks optimization algorithm. Neural Comput & Applic 34, 18341–18368 (2022). https://doi.org/10.1007/s00521-022-07367-2

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