Human Centered Optimization Lab Pymoo Dashboard
Advisor(s)
Dr. Ian Kropp
Confirmation
1
Document Type
Paper
Location
ONU McIntosh Center; Activities Room
Start Date
11-4-2025 10:00 AM
End Date
11-4-2025 10:50 AM
Abstract
The Human Centered Optimization Lab Pymoo Dashboard is an initiative aimed at enhancing accessibility to the Python-based multi-objective optimization library, Pymoo. While Pymoo offers extensive flexibility for optimizations, its command-line interface poses a challenge to users unfamiliar with such environments. To address this gap, we developed a web-based user interface designed for ease of use and simplicity.
My role in this project involved creating the main dashboard by decomposing it into manageable components and designing an intuitive way to integrate more complex features. Key contributions include revamping the backend processes that handle data communication between the frontend and backend systems, improving overall efficiency. The frontend was enhanced through componentization and performance optimization using Nuxt and websocket connections. Additionally, socket.io ensures compatibility for environments where websockets might not be supported.
This project aims to democratize access to advanced machine learning tasks by making Pymoo more user-friendly. By leveraging modern web technologies, we've created a platform that lowers the barrier of entry for users without technical backgrounds in command-line operations, thus broadening the reach and impact of multi-objective optimization tools.
Recommended Citation
Stock, Josh, "Human Centered Optimization Lab Pymoo Dashboard" (2025). ONU Student Research Colloquium. 27.
https://digitalcommons.onu.edu/student_research_colloquium/2025/Posters/27
Open Access
Available to all.
Human Centered Optimization Lab Pymoo Dashboard
ONU McIntosh Center; Activities Room
The Human Centered Optimization Lab Pymoo Dashboard is an initiative aimed at enhancing accessibility to the Python-based multi-objective optimization library, Pymoo. While Pymoo offers extensive flexibility for optimizations, its command-line interface poses a challenge to users unfamiliar with such environments. To address this gap, we developed a web-based user interface designed for ease of use and simplicity.
My role in this project involved creating the main dashboard by decomposing it into manageable components and designing an intuitive way to integrate more complex features. Key contributions include revamping the backend processes that handle data communication between the frontend and backend systems, improving overall efficiency. The frontend was enhanced through componentization and performance optimization using Nuxt and websocket connections. Additionally, socket.io ensures compatibility for environments where websockets might not be supported.
This project aims to democratize access to advanced machine learning tasks by making Pymoo more user-friendly. By leveraging modern web technologies, we've created a platform that lowers the barrier of entry for users without technical backgrounds in command-line operations, thus broadening the reach and impact of multi-objective optimization tools.