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

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-se...

Full description

Saved in:
Bibliographic Details
Published in:Engineering applications of artificial intelligence 2015-02, Vol.38, p.147-167
Main Authors: Carrano, Eduardo G., Gouveia Coelho, Dayanne, Gaspar-Cunha, António, Wanner, Elizabeth F., Takahashi, Ricardo H.C.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Benchmarking
Evolutionary computation
Expert systems
Extrusion
Genetic algorithms
Local search
Mathematical models
Multiobjective optimization
Operators
Optimization
Polymer extrusion
Sampling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2014.10.016