scholarly journals Models-based Optimization Methods for the Specication of Fuzzy Inference Systems in Discrete EVent Simulation

2011 ◽  
Author(s):  
Paul-Antoine Bisgambiglia ◽  
Bastien Poggi ◽  
Celine Nicolai
Keyword(s):  
Discrete Event Simulation ◽  
Fuzzy Inference ◽  
Discrete Event ◽  
Optimization Methods ◽  
Fuzzy Inference Systems ◽  
Event Simulation ◽  
Inference Systems

Comparison of Selected Optimization Methods Used for Discrete Event Simulation Models and Testing Functions

2013 ◽  
Vol 816-817 ◽  
pp. 629-633
Author(s):  
Pavel Raska ◽  
Ulrych Zdenek
Keyword(s):  
Discrete Event Simulation ◽  
Evaluation Method ◽  
Random Search ◽  
Discrete Event ◽  
Simulation Models ◽  
Optimization Methods ◽  
Global Optimum ◽  
Simulation Software ◽  
Hill Climbing ◽  
Event Simulation

The paper deals with the comparison of selected optimization methods - Random Search, Hill Climbing, Tabu Search, Local Search, Downhill Simplex, Simulated Annealing, Differential Evolution and Evolution Strategy-used to search for the global optimum of the objective function specified for each simulation model. These optimization methods have to be modified in such a way that they are applicable for discrete event simulation optimization purposes. Three discrete event simulation models were built (using ARENA simulation software) which reflect real industrial systems. Then the optimization methods were tested on four testing functions. The evaluation method which uses information from the box plot characteristics was specified.

scholarly journals Dynamic reliability assessment of a complex recovery system using fault tree, fuzzy inference and discrete event simulation

2021 ◽  
Vol 23 (4) ◽  
pp. 593-604
Author(s):  
Abolghasem Nobakhti ◽  
Sadigh Raissi ◽  
Kaveh Khalili Damghani ◽  
Roya Soltani
Keyword(s):  
Discrete Event Simulation ◽  
Fuzzy Inference ◽  
Discrete Event ◽  
Fault Tree ◽  
Reliability Assessment ◽  
Operating Conditions ◽  
Operating Pressure ◽  
Dynamic Reliability ◽  
Event Simulation ◽  
Recovery System

Any failure on the recovery system will cause a lot of environmental damage as well as energy loss. Hereof two types of alternatives; fast opening valve system (FOVS) and seal drum system (SDS) may be installed. The focus of this article is on the decision stage to choose the most preferred option in terms of reliability assessment. The major challenge in the research problem is on changing the pressure and temperature during operational cycles, which significantly affect the reliability. In addition, the lack of historical data complicates the reliability assessment method. Hence, we proposed a hybrid approach using fault tree analysis (FTA) and the Mamdani fuzzy inference to estimate reliability response as a function of a few frequently operating pressure and temperature. Also, discrete-event simulation helped us to evaluate the system reliability at different operating conditions. The comparisons reveals that the FOVs outperforms on average of 22.4% than the SDS and it is recommended for putting into practice for purchasing.

scholarly journals Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
Jose Soria ◽  
Emanuel Ontiveros-Robles ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fuzzy Inference ◽  
Harmony Search ◽  
Dynamic Parameter ◽  
Optimization Methods ◽  
Dynamic Adaptation ◽  
Fuzzy Inference Systems ◽  
Parameter Adaptation ◽  
Inference Systems

Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.

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