Research Area: Metaheuristic Computing
Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin’s huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.
Keywords:
Emperor Penguin Optimizer
huddling behaviour
features
benchmark functions
optimization algorithm
Author(s) Name: Gaurav Dhiman, Diego Oliva, Amandeep Kaur, Krishna Kant Singh, S. Vimal, Ashutosh Sharma, Korhan Cengiz
Journal name: Knowledge-Based Systems
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.knosys.2020.106560
Volume Information: Volume 211
