Solving Multi-Objective Optimization Problems with the Pymoo Library
Advisor(s)
Dr. Ian Kropp
Confirmation
1
Document Type
Poster
Location
McIntosh Activities Room
Start Date
19-4-2024 12:00 PM
End Date
12-4-2024 12:50 PM
Abstract
Solving multi-objective problems, or finding the optimal solution or "trade-off", has been a challenging feat in many disciplines of human society. With Evolutionary Multi-Objective Optimization (EMO) researchers have developed a way to model such problems to find feasible solutions by using populations that evolve from one another by crossing-over, mutation, and selection of the most dominant solutions in the pareto front. To research how EMO performs, I utilize the pymoo library in the Python language and Jupyter Notebook to analyze test problems and how pymoo detects optimal solutions. Additionally, I use the pymoo library to develop an algorithm to find the solution to a given Sudoku puzzle by utilizing the Genetic Algorithm and developing constraints to find the optimal solution.
Recommended Citation
Lyons, Maria, "Solving Multi-Objective Optimization Problems with the Pymoo Library" (2024). ONU Student Research Colloquium. 35.
https://digitalcommons.onu.edu/student_research_colloquium/2024/Posters/35
Level of Access
Restricted to ONU Community
Restricted
Available to ONU community via local IP address and ONU login.
Solving Multi-Objective Optimization Problems with the Pymoo Library
McIntosh Activities Room
Solving multi-objective problems, or finding the optimal solution or "trade-off", has been a challenging feat in many disciplines of human society. With Evolutionary Multi-Objective Optimization (EMO) researchers have developed a way to model such problems to find feasible solutions by using populations that evolve from one another by crossing-over, mutation, and selection of the most dominant solutions in the pareto front. To research how EMO performs, I utilize the pymoo library in the Python language and Jupyter Notebook to analyze test problems and how pymoo detects optimal solutions. Additionally, I use the pymoo library to develop an algorithm to find the solution to a given Sudoku puzzle by utilizing the Genetic Algorithm and developing constraints to find the optimal solution.