Genetic Algorithm to Estimate Cumulative Prospect Theory Parameters for Selection of High-Occupancy-Vehicle Lane

2010 ◽  
Vol 2157 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Joseph Y. J. Chow ◽  
Gunwoo Lee ◽  
Inchul Yang
Keyword(s):  
Genetic Algorithm ◽  
Prospect Theory ◽  
Cumulative Prospect Theory ◽  
Selection Of

Optimal selection of energy storage nodes based on improved cumulative prospect theory in China

2020 ◽  
Vol 12 (6) ◽  
pp. 064101
Author(s):  
Jicheng Liu ◽  
Zhenzhen Wang ◽  
Yu Yin ◽  
Yinghuan Li ◽  
Yunyuan Lu
Keyword(s):  
Energy Storage ◽  
Prospect Theory ◽  
Cumulative Prospect Theory ◽  
Optimal Selection ◽  
Selection Of

scholarly journals Extended GRA Method Based on Cumulative Prospect Theory with Probabilistic Hesitant Fuzzy Information for MAGDM and its Application to Green Supplier Selection

2021 ◽  
Author(s):  
Ningna Liao ◽  
Guiwu Wei ◽  
Xudong Chen
Keyword(s):  
Prospect Theory ◽  
Simulation Analysis ◽  
Cumulative Prospect Theory ◽  
Decision Makers ◽  
Risk Appetite ◽  
Probabilistic Values ◽  
Grey Relational ◽  
Decision Elements ◽  
Different Levels ◽  
Selection Of

Abstract An extended grey relational analysis (GRA) method is introduced in this article to reduce the limitations of the classical GRA method using the cumulative prospect theory (CPT) which takes into account psychological factors such as the risk appetite of decision makers. Moreover, the circumstance of probabilistic hesitant fuzzy (PHF) which assigns probabilistic values to DMs’ different levels of hesitation shows its superiority when making decisions in a complex environment. Meanwhile the weighting vector of each attribute is calculated according to the entropy which is calculated by the different prospect decision elements. Thus, in this paper, we proposed an extended GRA method based on cumulative prospect theory in the probabilistic hesitant fuzzy circumstance and applying the model in the selection of the green supplier. At last, the comparative analysis and the simulation analysis are made to show the practicability of this newly proposed method.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Sign in / Sign up

Export Citation Format

Share Document