Generalized Extremal Optimization for Solving Complex Optimal Design Problems

2003 ◽  
pp. 375-376 ◽  
Author(s):  
Fabiano Luis de Sousa ◽  
Valeri Vlassov ◽  
Fernando Manuel Ramos
Keyword(s):  
Optimal Design ◽  
Design Problems ◽  
Extremal Optimization ◽  
Generalized Extremal Optimization

GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster

2011 ◽  
Author(s):  
Roberto Luiz Galski ◽  
Heitor Patire Ju´nior ◽  
Fabiano Luis de Sousa ◽  
Jose´ Nivaldo Hinckel ◽  
Pedro Lacava ◽  
...  
Keyword(s):  
Temperature Profile ◽  
Film Cooling ◽  
Simulated Data ◽  
Convergence Properties ◽  
Extremal Optimization ◽  
Hybrid Optimization Algorithm ◽  
Design Variables ◽  
Desirable Feature ◽  
Self Tuning ◽  
Generalized Extremal Optimization

In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.

Optimal Design of a Gas Turbine Cogeneration Plant by a Hierarchical Optimization Method With Parallel Computing

2018 ◽  
Author(s):  
Ryohei Yokoyama ◽  
Yuji Shinano ◽  
Yuki Wakayama ◽  
Tetsuya Wakui
Keyword(s):  
Optimal Design ◽  
Energy Supply ◽  
Optimization Method ◽  
Optimal Operation ◽  
Hierarchical Optimization ◽  
Cogeneration Plant ◽  
Design Problems ◽  
Computation Efficiency ◽  
Supply Systems ◽  
Energy Supply Systems

To attain the highest performance of energy supply systems, it is necessary to rationally determine types, capacities, and numbers of equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. Mixed-integer linear programming (MILP) approaches have been applied widely to such optimal design problems. The authors have proposed a MILP method utilizing the hierarchical relationship between design and operation variables to solve the optimal design problems of energy supply systems efficiently. In addition, some strategies to enhance the computation efficiency have been adopted: bounding procedures at both the levels and ordering of the optimal operation problems at the lower level. In this paper, as an additional strategy to enhance the computation efficiency, parallel computing is adopted to solve multiple optimal operation problems in parallel at the lower level. In addition, the effectiveness of each and combinations of the strategies adopted previously and newly is investigated. This hierarchical optimization method is applied to an optimal design of a gas turbine cogeneration plant, and its validity and effectiveness are clarified through some case studies.

Multiobjective generalized extremal optimization algorithm for simulation of daylight illuminants

2017 ◽  
Vol 7 (04) ◽  
pp. 1
Author(s):  
Srividya Ravindra Kumar ◽  
Ciji Pearl Kurian ◽  
Marcos Eduardo Gomes-Borges
Keyword(s):  
Optimization Algorithm ◽  
Extremal Optimization ◽  
Generalized Extremal Optimization

scholarly journals A Modular Framework for the Modelling and Optimization of Advanced Chromatographic Processes

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 65 ◽  
Author(s):  
Johannes Schmölder ◽  
Malte Kaspereit
Keyword(s):  
Optimal Design ◽  
Case Studies ◽  
Design Problems ◽  
Column Model ◽  
Current Implementation ◽  
Systematic Development ◽  
Model Equations ◽  
Modelling And Optimization ◽  
Modelling Simulation ◽  
Modular Framework

A framework is introduced for the systematic development of preparative chromatographic processes. It is intended for the optimal design of conventional and advanced concepts that exploit strategies, such as recycling, side streams, bypasses, using single or multiple columns, and combinations thereof. The Python-based platform simplifies the implementation of new processes and design problems by decoupling design tasks into individual modules for modelling, simulation, assertion of cyclic stationarity, product fractionation, and optimization. Interfaces to external libraries provide flexibility regarding the choice of column model, solver, and optimizer. The current implementation, named CADET-Process, uses the software CADET for solving the model equations. The structure of the framework is discussed and its application for optimal design of existing and identification of new chromatographic operating concepts is demonstrated by case studies.

Heat Pipe Design Through Generalized Extremal Optimization

2004 ◽  
Vol 25 (7) ◽  
pp. 34-45 ◽  
Author(s):  
FABIANO LUIS DE SOUSA ◽  
VALERI V. VLASSOV ◽  
FERNANDO MANUEL RAMOS
Keyword(s):  
Heat Pipe ◽  
Extremal Optimization ◽  
Generalized Extremal Optimization
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