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A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

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Abstract

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

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Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

  2. Division of Engineering, New York University Abu Dhabi, 129188, Abu Dhabi, Saadiyat Island, UAE

    Ali Diabat

  3. Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Ali Diabat

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  1. Laith Abualigah
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Abualigah, L., Diabat, A. A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput & Applic 32, 15533–15556 (2020). https://doi.org/10.1007/s00521-020-04789-8

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