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

  • Shahrzad Saremi
  • Seyedehzahra Mirjalili
  • Seyedali MirjaliliEmail author
  • Jin Song Dong
Chapter
Part of the Studies in Computational Intelligence book series (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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shahrzad Saremi
    • 1
  • Seyedehzahra Mirjalili
    • 3
  • Seyedali Mirjalili
    • 1
    Email author
  • Jin Song Dong
    • 1
    • 2
  1. 1.Institute of Integrated and Intelligent Systems, Griffith UniversityNathan, BrisbaneAustralia
  2. 2.Department of Computer ScienceSchool of Computing, National University of SingaporeSingaporeSingapore
  3. 3.School of Electrical Engineering and ComputingUniversity of NewcastleCallaghanAustralia

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