Optimization techniques for the selection of members and attributes in ensemble systems

2011 ◽  
Author(s):  
Antonino Feitosa Neto ◽  
Anne M P Canuto ◽  
Elizabeth F G Goldbarg ◽  
Marco C Goldbarg
Keyword(s):  
Optimization Techniques ◽  
Ensemble Systems ◽  
Selection Of

Filter-based optimization techniques for selection of feature subsets in ensemble systems

2014 ◽  
Vol 41 (4) ◽  
pp. 1622-1631 ◽  
Author(s):  
Laura Emmanuella A. dos S. Santana ◽  
Anne M. de Paula Canuto
Keyword(s):  
Optimization Techniques ◽  
Ensemble Systems ◽  
Selection Of

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

scholarly journals Prediction and analysis of penetration rate in drilling operation using deterministic and metaheuristic optimization methods

2021 ◽  
Author(s):  
Ibrahim Sobhi ◽  
Abdelmadjid Dobbi ◽  
Oussama Hachana
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Computational Time ◽  
Objective Functions ◽  
Memory Space ◽  
Drilling Operation ◽  
Drilling Parameters ◽  
Selection Of

AbstractThe rate of penetration (ROP) optimization is one of the most important factors in improving drilling efficiency, especially in the downturn time of oil prices. This process is crucial in the well planning and exploration phases, where the selection of the drilling bits and parameters has a significant impact on the total cost and time of the drilling operation. Thus, the optimization and best selection of the drilling parameters are critical. Optimization of ROP is difficult due to the complexity of the relationship between the drilling variables and the ROP. For this reason, the development of high-performance computer systems, predictive models, and algorithms will be the best solution. In this study, a new investigation approach for ROP optimization has been done regarding different ROP models (Maurer, Bingham, Bourgoyne and Young models), algorithms (Multiple regression, ant colony optimization (ACO), fminunc, fminsearch, fsolve, lsqcurvefit, lsqnonlin), and different objective functions. The well-known data from the Louisiana field in an offshore well have been used to compare the used parameter estimation approach with other techniques. Indeed, datasets from an onshore well in the Hassi Messaoud Algerian field are explored. The results confirmed the superiority and the effectiveness of B&Y models compared to Bingham and Maurer models. Fminsearch, lsqcurvefit, ACO, and Excel (GRG) algorithms give the best results in ROP prediction while the application of the MNLR approach. Using the mean squared error (MSE) and the determination coefficient (R$$^{2}$$ 2 ) as objective functions significantly increases the accuracy prediction where the results given are ($$R=0.9522$$ R = 0.9522 , $$RMSE=2.85$$ R M S E = 2.85 ) and ($$R= 0.9811$$ R = 0.9811 , $$RMSE=4.08$$ R M S E = 4.08 ) for Wells 1 and 2, respectively. This study validates the application of B&Y model in both onshore and offshore wells. The findings reveal to deal with data limitation problems in ROP prediction. Simple and effective optimization techniques that require less memory space and computational time have been provided.

Components for Composite Material Customization System

2009 ◽  
Author(s):  
Soumitra Nandi ◽  
Zahed Siddique
Keyword(s):  
Composite Materials ◽  
Material Selection ◽  
Selection Process ◽  
Material Design ◽  
Optimization Techniques ◽  
Conventional Approach ◽  
Wide Range ◽  
Properties Of Materials ◽  
Selection Of

With the advancements of composite materials and research in nano-composites, designers have the flexibility to select materials from a wide range of properties to meet their specific design needs. Even with all these advancements, the material selection process during design follows a very conventional approach. The conventional approach to material design is to select a certain material from a given pre-set material list that allows the attainment of nearest properties required for the product. One of the disadvantages of this approach is that the trade-off inherent in the selection of material, when multiple properties are targeted, can be cumbersome to achieve or addressed at all. In this paper we present an approach to select and design composite materials, where the designer will have flexibility to select multiple properties of materials during the design of a new product. This approach employs an index for selection combined with heuristic optimization techniques to select the optimized combination of composite materials that could meet closest possible property goals. In the case study presented in this paper, we did not perform any optimization; rather, emphasize is given to the explanation of material selection technique, and an RMS value is introduced as an index for the selection.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

A Design Methodology for Obtaining Infinite Stiffness in an Aerostatic Thrust Bearing

1986 ◽  
Vol 108 (4) ◽  
pp. 448-453 ◽  
Author(s):  
Michael R. Bryant ◽  
Steven A. Velinsky ◽  
Norman H. Beachley ◽  
Frank J. Fronczak
Keyword(s):  
Scaling Laws ◽  
Thrust Bearing ◽  
Design Procedure ◽  
Optimization Techniques ◽  
Positional Accuracy ◽  
Design Algorithm ◽  
Flexible Membrane ◽  
Plate Equations ◽  
Main Components ◽  
Selection Of

The desire to maintain high positional accuracy has led to many unique bearing designs which achieve high, and even infinite, stiffness. This paper presents a design procedure for an aerostatic thrust bearing and demonstrates that infinite stiffness may be attained by proper selection of the bearing’s geometric parameters. The bearing is comprised of two main components; the bearing housing and a flexible membrane that deflects due to changes in load. The current analysis employs plate equations to approximate the behavior of the membrane. Optimization techniques are utilized in the design algorithm and the results are presented in the form of a design chart or nomograph. Additionally, so that the results may be extended to other bearing sizes and gas properties, scaling laws are developed based on a dimensional analysis of the associated variables.

scholarly journals Planning multi-terminal direct current grids based graphs theory

2021 ◽  
Vol 11 (1) ◽  
pp. 37
Author(s):  
Mario A. Rios ◽  
Fredy A. Acero
Keyword(s):  
Power Systems ◽  
Direct Current ◽  
Optimization Techniques ◽  
Large Power ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Ac Power ◽  
Hvdc Line ◽  
Selection Of

Transmission expansion planning in AC power systems is well known and employs a variety of optimization techniques and methodologies that have been used in recent years. By contrast, the planning of HVDC systems is a new matter for the interconnection of large power systems, and the interconnection of renewable sources in power systems. Although the HVDC systems has evolved, the first implementations were made considering only the needs of transmission of large quantities of power to be connected to the bulk AC power system. However, for the future development of HVDC systems, meshed or not, each AC system must be flexible to allow the expansion of these for future conditions. Hence, a first step for planning HVDC grids is the planning and development of multi-terminal direct current (MTDC) systems which will be later transformed in a meshed system. This paper presented a methodology that use graph theory for planning MTDC grids and for the selection of connection buses of the MTDC to an existing HVAC transmission system. The proposed methodology was applied to the Colombian case, where the obtained results permit to migrate the system from a single HVDC line to a MTDC grid.

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