Next Article in Journal
High-Capacity Data-Hiding Scheme on Synthesized Pitches Using Amplitude Enhancement—A New Vision of Non-Blind Audio Steganography
Next Article in Special Issue
A Novel Single-Valued Neutrosophic Set Similarity Measure and Its Application in Multicriteria Decision-Making
Previous Article in Journal
Towards Secure Data Retrieval for Multi-Tenant Architecture Using Attribute-Based Key Word Search
Previous Article in Special Issue
Some Single-Valued Neutrosophic Dombi Weighted Aggregation Operators for Multiple Attribute Decision-Making
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Symmetry 2017, 9(6), 87; doi:10.3390/sym9060087

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]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top