Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network

Spotted hyena optimizer (SHO) is a recently developed swarm-based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)-SHO. In STS-SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real-world application, the said algorithm has been applied to train pi-sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS-SHO is an effective and trustworthy algorithm, which has the ability to resolve real-life optimization complications.