Multi-objective Optimization

Multi-objective optimization is applied if multiple conflicting objective occur. Considering these objective simultaneously leads to a set of Pareto optimal solutions for choosing the best production design.

Best Practice


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Visualization of the Pareto plot

  • Detection and evaluation of conflicting objectives
  • Verification of conflicting objectives by single optimizations with weighted objective functions
  • Integration of the previous knowledge obtained from sensitivity analyses and weighted optimizations into the initial function of Pareto optimization




Vizualization of the Pareto front designs

  • Use of evolutionary algorithms and Particle Swarm Optimizations
  • Fitness assignment using dominance-based ranking
  • Dominance based constraint handling
  • Verification of diversity by density estimation

Postprocessing & Visualization


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  • Visualization of the objective space
  • Selection of 2D or 3D subspace visualizations
  • Parallel coordinate plots and cluster analysis for best design selection


Practical Application Examples


practical application examples


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