Feedback-control operators for improved Pareto-set description: application to a polymer extrusion process

Eduardo G. Carrano, Dayanne Gouveia Coelho, António Gaspar-Cunha, Elizabeth F. Wanner, Ricardo H.C. Takahashi

Research output: Contribution to journalArticlepeer-review


This paper presents a new class of operators for multiobjective evolutionary algorithms that are inspired on feedback-control techniques. The proposed operators, the archive-set reduction and the surface-filling crossover, have the purpose of enhancing the quality of the description of the Pareto-set in multiobjective optimization problems. They act on the Pareto-estimate sample set, performing operations that eliminate archive points in the most crowded regions, and generate new points in the less populated regions, leading to a dynamic equilibrium that tends to generate a uniform sampling of the efficient solution set. The internal parameters of those operators are coordinated by feedback-control inspired techniques, which ensure that the desired equilibrium is attained. Numerical experiments in some benchmark problems and in a real problem of optimization of a single screw extrusion system for polymer processing show that the proposed methodology is able to generate more detailed descriptions of Pareto-optimal fronts than the ones produced by usual algorithms.

Original languageEnglish
Pages (from-to)147-167
Number of pages21
JournalEngineering Applications of Artificial Intelligence
Early online date26 Nov 2014
Publication statusPublished - Feb 2015


  • evolutionary computation
  • genetic algorithms
  • local search
  • multiobjective optimization
  • polymer extrusion


Dive into the research topics of 'Feedback-control operators for improved Pareto-set description: application to a polymer extrusion process'. Together they form a unique fingerprint.

Cite this