Optimize multi-objective engineering designs
pymooskillsetup L3★27,559
K-Dense-AI/scientific-agent-skills ↗What it does
Find Pareto-optimal solutions for multi-objective optimization
Best for
When you need to explore trade-offs among 2+ conflicting objectives and choose based on domain knowledge.
Inputs
- · problem definition (objectives, constraints)
- · algorithm choice (NSGA-II/III)
- · termination criteria
Outputs
- · Pareto front (result.F)
- · decision variables (result.X)
- · visualization + decision-making
Requires
- · Python 3.10+
- · pymoo 0.6.1.6
- · NumPy
- · SciPy
- · matplotlib (optional)
Preconditions
- · problem defined as Problem / ElementwiseProblem / FunctionalProblem
- · objectives/constraints math clear
Failure modes
- · single-objective algorithm on multi-objective problem
- · reference directions missing for NSGA-III
Trust signals
- · benchmark on ZDT/DTLZ problems
- · Pareto front visually dominates random baseline