Pymoo Dashboard: An Optimized Internet Dashboard for Algorithm Visualization and Performance Tracking
Advisor(s)
Dr. Ian Kropp
Confirmation
1
Document Type
Poster
Location
ONU McIntosh Center; Activities Room
Start Date
11-4-2025 12:00 PM
End Date
11-4-2025 12:50 PM
Abstract
In a world of constantly changing technological advancements, users everywhere are looking for quicker, smoother ways to enjoy the internet. Therefore, the need for optimizations in the performance of technology is rapidly increasing. This project focuses on addressing that need by creating an optimization web application that aids user needs within the field. Our current design focuses on displaying optimization algorithms, such as the NSGA algorithms, through their various evolutionary stages. My hypothesis is that with adjustments to our current design, such as changing file types to promote quicker data processing speeds, and developing the project to provide more interface responsiveness and insight to the selected focus of our test algorithms, we will be able to effectively create a scalable web interface that can be universally implemented for various types of optimization work.
Recommended Citation
Rennie, Rachael, "Pymoo Dashboard: An Optimized Internet Dashboard for Algorithm Visualization and Performance Tracking" (2025). ONU Student Research Colloquium. 76.
https://digitalcommons.onu.edu/student_research_colloquium/2025/Posters/76
Open Access
Available to all.
Pymoo Dashboard: An Optimized Internet Dashboard for Algorithm Visualization and Performance Tracking
ONU McIntosh Center; Activities Room
In a world of constantly changing technological advancements, users everywhere are looking for quicker, smoother ways to enjoy the internet. Therefore, the need for optimizations in the performance of technology is rapidly increasing. This project focuses on addressing that need by creating an optimization web application that aids user needs within the field. Our current design focuses on displaying optimization algorithms, such as the NSGA algorithms, through their various evolutionary stages. My hypothesis is that with adjustments to our current design, such as changing file types to promote quicker data processing speeds, and developing the project to provide more interface responsiveness and insight to the selected focus of our test algorithms, we will be able to effectively create a scalable web interface that can be universally implemented for various types of optimization work.