Discrete optimization with fuzzy constraints

Primož Jelušič (Author), Bojan Žlender (Author)

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.

Keywords

negotovost;diskretno optimiranje;sinteza konstrukcij;optimizacija;uncertainty;discrete optimization;neuro-fuzzy technique;structural optimization;

Data

Language:	English
Year of publishing:	2017
Typology:	1.01 - Original Scientific Article
Organization:	UM FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
UDC:	624.04:004.8
COBISS:	20691222
ISSN:	2073-8994
Parent publication:	Symmetry
Views:	937
Downloads:	332
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	negotovost;diskretno optimiranje;sinteza konstrukcij;optimizacija;
URN:	URN:SI:UM:
Type (COBISS):	Scientific work
Pages:	str. 1-22
Volume:	ǂVol. ǂ9
Issue:	ǂiss. ǂ6
Chronology:	June 2017
DOI:	10.3390/sym9060087
ID:	10852869