Solving the fuzzy shortest path problem using multi-criteria decision method based on vague similarity measure

Dou, Yaling; Zhu, Lichun; Wang, Simon Ho

doi:10.1016/j.asoc.2012.03.013

Cited by 39 publications

(22 citation statements)

References 41 publications

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“…Various definitions for (dis)similarity have been proposed in the literature in various data domains [3], [4], [25] without consensus. Motivated by a popular definition of similarity between fuzzy sets [25], we propose the following definition in a mathematical lattice.…”

Section: Similarity Measures On Latticesmentioning

confidence: 99%

“…Another approach for dealing with nonnumerical data is by developing domain-specific (mathematical) tools. Drawbacks of the latter approach include: first, different mathematical tools need to be "ad-hoc" devised in different (nonnumerical) data domains and, second, performance cannot, often, be tuned [3], [4]. Yet another approach has been proposed lately based on mathematical lattice theory as explained next.…”

Section: Introductionmentioning

confidence: 97%

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Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Kaburlasos

Papakostas

2015

IEEE Comput. Intell. Mag.

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This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice. In particular, the interest here focuses on lattices of (Type-1) Inter vals' Numbers (INs), where an IN represents a distribution of image features including orthogonal moments. A neural classif ier, namely fuzzy lattice reasoning (f lr) fuzzy-ART-MAP (FAM), or f lrFAM for short, is described for learning distributions of INs; hence, Type-2 INs emerge. Four benchmark image pattern recognition applications are demonstrated. The results obtained by the proposed techniques compare well with the results obtained by alternative methods from the literature. Furthermore, due to the isomorphism between the lattice of INs and the lattice of fuzzy numbers, the proposed techniques are straightforward applicable to Type-1 and/or Type-2 fuzzy systems. The far-reaching potential for deep learning in big data applications is also discussed.

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Section: Similarity Measures On Latticesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Kaburlasos

Papakostas

2015

IEEE Comput. Intell. Mag.

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“…The literature [10] proposed a multi-criteria path selection method with the multi-objective optimization based on the vague similarity measure. In the method, multiple metrics (e.g., delay, bandwidth, hop count, and packet loss) in a path are considered as a vague set, and the vague values are partitioned into seven fuzzy levels.…”

Section: Related Workmentioning

confidence: 99%

A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

Gong

Yang

Zhang

et al. 2015

Int J Communication

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Current Internet Protocol routers only support equal cost multi-path routing, which performs the random path selection or the traffic uniform distribution among equal-cost paths. In biology, an adaptive attractor selection model is presented to simulate the concentration changes of two kinds of Escherichia coli's mRNA in changing nutrition environments with bistability equations. Inspired by the metabolism behaviors of E. coli, we propose an adaptive path selection scheme Open Shortest Path First-path selection by attractor selection to dynamically select the transmission path by the real-time path quality. Here, the mRNA concentration is analogous to the path quality. Then, to reflect the multipath quality, multi-stability equations are adopted and redesigned. Our scheme consists of two main features. The first one is a redefined path-activity to indicate multipath transmission goodness, which is inversely proportional to the offset between current path quality and best path quality. And the second one is a new attractor expression of the multi-stability equations to concretely specify the effect of a stochastic item noise in the equations on the path selection. Compared with the greedy selection and the uniform random selection in file transfer protocol (FTP) service, our scheme gains better performance on reducing file transmission time, traffic throughput, and traffic dropped.the adaptive attractor selection model [8] is introduced into the field of the computer networks to optimize the multipath traffic transmission.Originally, the attractor selection model [9] is proposed to simulate the metabolism behaviors of the Escherichia coli in the dynamic nutrition environments with the revised bistability equations. In nature, each individual E. coli always regulates adaptively its own state in the different nutrition environments. When nutrition is adequate, the metabolism of the E. coli becomes vigorous generally. Then, the mRNA concentrations in the E. coli greatly increase to produce enough nutrition materials for growth and reproduction. Furthermore, the vigorous state will be kept if nutrition is adequate. Subsequently, when nutrition is inadequate, the metabolism of the E. coli is weak. Then, the mRNA concentrations inevitably decrease. Moreover, the concentrations are able to change stochastically to make the E. coli shift to a suitable metabolism state in the current nutrition environment [9]. Mathematically, this adaptation process of the E. coli to the changing nutrition environments can be modeled by the bistability equations, in which each attractor formulates a stable adaptation state of the mRNA concentrations.There are two key parameters in the bistability equations. One is called activity, which indicates the adaptation goodness of the E. coli to the nutrition environment. And the other one is noise, which takes the stochastic effect on the mRNA concentrations. The two parameters have a relation of ebb and flow. When the activity increases, the stochastic effect of the noise on the mRNA concentrati...

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“…[2][3]. To provide Quality of Service (QoS) assurance for the stated applications it is necessary to consider the uncertainty involved in the network environment as the parameters involved like cost, time, delay are not naturally precise and the uncertainty can be modeled by using Fuzzy numbers leading to Fuzzy Constrained Shortest Path Problem (FCSPP) [3].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy constrained shortest path algorithm using circumcenter of centroids

Verma

Shukla

2013

2013 3rd IEEE International Advance Computing Conference (IACC)

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The constrained shortest path problem (CSPP) is a well known NP-Complete problem where the task is to determine the shortest path that satisfies certain constrains like delay and cost. Other than the straight-forward implementation in the area of networking, this problem also finds application in the field of multimedia, crew scheduling etc. In these kinds of applications it is important to take into consideration the uncertainty involved in the network environment to provide the Quality of Service (QoS) assurance which can be modeled by the use of fuzzy numbers for the representation of the parameters involved. In this paper, we extend the algorithm stated by Sahni et al to deal with the fuzzy constrained shortest path problem (FCSPP) by fuzzifying one of the constraints i.e. cost and representing it as a trapezoidal fuzzy number. Fuzzy numbers cannot be ordered like real numbers so the Circumcenter of Centroid method is used to rank the trapezoidal fuzzy numbers and determine the fuzzy constrained shortest path also taking into account the delay involved.Keywords-constrained shortest path, fuzzy constrained shortest path, trapezoidal fuzzy numbers, circumcenter of centroid method, path delay discretization.

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Solving the fuzzy shortest path problem using multi-criteria decision method based on vague similarity measure

Cited by 39 publications

References 41 publications

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

Fuzzy constrained shortest path algorithm using circumcenter of centroids

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