ABSTRACT
This chapter discusses practical aspects of the algorithm inspired by the social behavior of grasshoppers -- insects which are known for their tendency to form huge swarms. The technique which mimics their movement strategies known as Grasshopper Optimization Algorithm relies on three components resembling the influence of social interactions and the wind advection.
The first part of this chapter presents the original version of the algorithm aimed at solving continuous optimization tasks. It is followed by the description of modifications -- adapting its scheme to the alternative optimization domains, using different movement strategies or treating GOA as a component of hybrid metaheuristic. Finally, we also present demonstrative use case of GOA for data clustering, ubiquitous task of unsupervised machine learning.
