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Symmetry 2017, 9(6), 87;

Discrete Optimization with Fuzzy Constraints

Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
Author to whom correspondence should be addressed.
Academic Editor: José Carlos R. Alcantud
Received: 8 May 2017 / Revised: 5 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Fuzzy Techniques for Decision Making)
View Full-Text   |   Download PDF [1126 KB, uploaded 23 June 2017]   |  


The 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
Keywords: uncertainty; discrete optimization; neuro-fuzzy technique; structural optimization uncertainty; discrete optimization; neuro-fuzzy technique; structural optimization

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Jelušič, P.; Žlender, B. Discrete Optimization with Fuzzy Constraints. Symmetry 2017, 9, 87.

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