کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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380519 | 1437441 | 2015 | 21 صفحه PDF | دانلود رایگان |
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.
Journal: Engineering Applications of Artificial Intelligence - Volume 38, February 2015, Pages 147–167