Guided stochastic search technique for discrete sizing optimization of steel trusses: A design-driven heuristic approach

2014 ◽  
Vol 134 ◽  
pp. 62-74 ◽  
Author(s):  
S. Kazemzadeh Azad ◽  
O. Hasançebi ◽  
M.P. Saka
Keyword(s):  
Stochastic Search ◽  
Heuristic Approach ◽  
Search Technique ◽  
Sizing Optimization ◽  
Steel Trusses ◽  
Stochastic Search Technique

scholarly journals Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 273
Author(s):  
Carmenza Moreno Roa ◽  
Adolfo Andrés Jaramillo Matta ◽  
Juan David Bastidas Rodríguez
Keyword(s):  
Experimental Data ◽  
Induction Motor ◽  
Stochastic Search ◽  
The Other ◽  
Objective Functions ◽  
Search Technique ◽  
Single Cage ◽  
New Strategy ◽  
A New Technique ◽  
Stochastic Search Technique

This paper deals with the implementation of a new technique of stochastic search to find the best set of parameters in a mathematical model, applied to the single cage (SC) model of the induction motor (IM). The technique includes a new strategy to generate variable constraints of the domain, seven error functions, weight for the operating zones of the IM, and multi-objective functions. The results are validated with experimental data of the torque and current in an IM, and show better fitting to the experimental curves compared with the results of two different techniques, one deterministic and the other one stochastic. The results obtained allow us to conclude that the best set of parameters for the model depends on the weights assigned to the objective functions and to the operating zones.

AGILE MODELING AND OPTIMIZATION OF END MILLING

2009 ◽  
Vol 08 (01) ◽  
pp. 71-80
Author(s):  
K. HANS RAJ ◽  
RAHUL SWARUP SHARMA ◽  
VIKAS UPADHYAY ◽  
ALOK K. VERMA
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
End Milling ◽  
Fuzzy Model ◽  
Stochastic Search ◽  
Average Surface Roughness ◽  
Search Technique ◽  
Machining Time ◽  
Neuro Fuzzy ◽  
Stochastic Search Technique

The rising demand for precision and quality in manufacturing necessitates that vast amounts of manufacturing knowledge be incorporated in manufacturing systems. Surface finish in end milling depends upon a number of variables such as cutting speed, feed rate, spindle speed, radial depth of cut, etc. The relative effect of these variables on surface roughness and machining time is quite considerable. A complex relationship exists between these process parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation of the average surface roughness and machining time based on process parameters. Neuro Fuzzy (NF) modeling has gained prominence recently on account of its fast reaction times, improved ease of operation and flexibility to respond to change in process parameters. In the present work, initially a Neuro Fuzzy Model is trained with experimental results of end milling. Subsequently, a generic approach is developed for optimization of end milling where the applicability and effectiveness of Neuro Fuzzy Model for function approximation is used to rapidly estimate average surface roughness and machining time in an integrated framework of Hybrid Stochastic Search Technique (HSST) to form a Neuro Fuzzy Hybrid Stochastic Search Technique (NFHSST). The results indicate that the NFHSST heuristic converges to better solutions rapidly as it provides the values of various process parameters for optimizing the objectives in a single run. Thus, NFHSST assists in the improvement of quality by developing multiple sound parts in an agile manner.

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