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Nature-Inspired Optimizers pp 107–122Cite as

Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

This chapter covers the fundamental concepts of the recently proposed Grasshopper Optimization Algorithm (GOA). The inspiration, mathematical model, and the algorithm are presented in details. A brief literature review of this algorithm including different variants, improvement, hybrids, and applications are given too. The performance of GOA is tested on a set of test functions including unimodal, multi-modal, and composite. The results show the ability of GOA in improving the quality of a random population, transiting from exploration to exploitation, showing high coverage of the search space, and accelerating the convergence curve over the course of iterations. The chapter also applies the GOA algorithm to a challenging problem in the field of hand posture estimation. It is observed that GOA finds an accurate configuration for a 3D hand model to match a given hand image acquired from a camera.

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Author information

Authors and Affiliations

  1. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Shahrzad Saremi, Seyedali Mirjalili & Jin Song Dong

  2. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  3. School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, 2308, Australia

    Seyedehzahra Mirjalili

Authors
  1. Shahrzad Saremi
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  2. Seyedehzahra Mirjalili
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  3. Seyedali Mirjalili
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  4. Jin Song Dong
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Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Saremi, S., Mirjalili, S., Mirjalili, S., Song Dong, J. (2020). Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_7

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