Discrete Optimization with Fuzzy Constraints
AbstractThe primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In this paper, heuristic fuzzy rules were used with the intention of improving the performance of optimization models, introducing experiential rules acquired from experts and utilizing recommendations. The aim of this paper was to define soft constraints using an adaptive network-based fuzzy inference system (ANFIS). This newly-developed soft constraint was applied to discrete optimization for obtaining optimal solutions. Even though the computational model is based on advanced computational technologies including fuzzy logic, neural networks and discrete optimization, it can be used to solve real-world problems of great interest for design engineers. The proposed computational model was used to find the minimum weight solutions for simply-supported laterally-restrained beams. View Full-Text
Share & Cite This Article
Jelušič, P.; Žlender, B. Discrete Optimization with Fuzzy Constraints. Symmetry 2017, 9, 87.
Jelušič P, Žlender B. Discrete Optimization with Fuzzy Constraints. Symmetry. 2017; 9(6):87.Chicago/Turabian Style
Jelušič, Primož; Žlender, Bojan. 2017. "Discrete Optimization with Fuzzy Constraints." Symmetry 9, no. 6: 87.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.