What problem does it solve? Engineering and design problems often involve conflicting objectives (cost vs. performance, weight vs. strength) with no single best answer. This Skill provides structured workflows for defining optimization problems, running evolutionary algorithms, and selecting trade-off solutions from Pareto fronts using the pymoo Python framework. ## Core Features & Use Cases - Multi-Objective Optimization: Run NSGA-II, NSGA-III, MOEA/D, and SPEA2 to compute Pareto fronts for problems with 2 to 15+ conflicting objectives. - Constraint Handling & Decision Making: Apply feasibility-first, penalty, or constraint-as-objective strategies, then select preferred solutions with MCDM methods like Pseudo-Weights and knee-point detection. - Benchmarks & Visualization: Test algorithms on ZDT, DTLZ, and WFG benchmark suites and visualize results with scatter plots, parallel coordinate plots, and petal diagrams. - Use Case: An engineer optimizing a structural design for minimum weight and maximum stiffness can define a custom ElementwiseProblem, run NSGA-II, visualize the Pareto front, and pick a balanced design using pseudo-weights. ## Quick Start Use the pymoo skill to solve a bi-objective optimization problem with NSGA-II and plot the resulting Pareto front.
