Related Experiment Video
Updated: Jun 13, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints
Published on: May 1, 2016
Comparative analysis of the gazelle Optimizer and its variants
Raghav Mahajan1, Himanshu Sharma1, Krishan Arora1
1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, India.
Abstract:
The Gazelle Optimization Algorithm (GOA) is an innovative nature-inspired metaheuristic algorithm, designed to mimic the agile and efficient hunting strategies of gazelles. Despite its promising performance in solving complex optimization problems, there is still a significant scope for enhancing its efficiency and robustness. This paper introduces several novel variants of GOA, integrating adaptive strategy, Levy flight strategy, Roulette wheel selection strategy, and random walk strategy. These enhancements aim to address the limitations of the original GOA and improve its performance in diverse optimization scenarios. The proposed algorithms are rigorously tested on CEC 2014 and CEC 2017 benchmark functions, five engineering problems, and a Total Harmonic Distortion (THD) minimization problem. The results demonstrate the superior performance of the proposed variants compared to the original GOA, providing valuable insights into their applicability and effectiveness.
Related Concept Videos
Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Quantifying and Rejecting Outliers: The Grubbs Test
Comparing the Survival Analysis of Two or More Groups
Friedman Two-way Analysis of Variance by Ranks
Column Efficiency: Rate Theory
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...
Production Efficiency

