scholarly journals Total Internal Reflection-Based High Efficiency Grating Design for a Metal-Free Polarizing Filter Applications Using Hybrid Optimization Procedure

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
ChaBum Lee
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Total Internal Reflection ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Internal Reflection ◽  
Hybrid Optimization ◽  
Design Parameters ◽  
Metal Free

This paper presents a fast and rigorous design method for grating-based metal-free polarizing filter applications using two-step hybrid optimization techniques. Grating structures utilizing the total internal reflection in a lamellar configuration were used to achieve metal-free solution, which is a key technology in the chirped pulse amplification for high power laser system. Here two polarizing filters were designed: polarization sensitive and polarization insensitive. Those polarization performances were characterized by the rigorous coupled-wave analysis (RCWA), and the design parameters of grating structures, pitch, depth, and filling factor were optimized by two-step hybrid optimization procedure because the diffraction characteristics of grating-based polarizing filters are highly sensitive to small changes in design parameters. The Taguchi method is incorporated into selection process in the genetic algorithm, which indicates that the Taguchi method optimizes the design parameters in a coarse manner, and then, coarsely optimized parameters are finely optimized using the genetic algorithm. Therefore the proposed method could solve global numerical optimization problems with continuous variables. The proposed two-step hybrid optimization algorithm could effectively optimize the grating structures for the purpose of polarization filter applications, and the optimized grating structures could selectively filter the incident light up to 99.8% as to TE or TM waves.

scholarly journals Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 494
Author(s):  
Ekaterina Andriushchenko ◽  
Ants Kallaste ◽  
Anouar Belahcen ◽  
Toomas Vaimann ◽  
Anton Rassõlkin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Permanent Magnet ◽  
Optimization Problems ◽  
Optimization Method ◽  
Taguchi Optimization ◽  
Torque Density ◽  
Electromagnetic Devices ◽  
3D Printed ◽  
Manufacturing Techniques

In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Optimization of Turbofan Propulsion Cycle Using a Genetic Algorithm

2010 ◽  
Author(s):  
Adel Ghenaiet
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Optimization Procedure ◽  
Specific Fuel Consumption ◽  
High Mass ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Simplifying Assumptions

This paper presents an evolutionary approach as the optimization framework to design for the optimal performance of a high-bypass unmixed turbofan to match with the power requirements of a commercial aircraft. The parametric analysis had the objective to highlight the effects of the principal design parameters on the propulsive performance in terms of specific fuel consumption and specific thrust. The design optimization procedure based on the genetic algorithm PIKAIA coupled to the developed engine performance analyzer (on-design and off-design) aimed at finding the propulsion cycle parameters minimizing the specific fuel consumption, while meeting the required thrusts in cruise and takeoff and the restrictions of temperatures limits, engine size and weight as well as pollutants emissions. This methodology does not use engine components’ maps and operates on simplifying assumptions which are satisfying the conceptual or early design stages. The predefined requirements and design constraints have resulted in an engine with high mass flow rate, bypass ratio and overall pressure ratio and a moderate turbine inlet temperature. In general, the optimized engine is fairly comparable with available engines of equivalent power range.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

Weight Optimization of Steel Frames Using Genetic Algorithm

2002 ◽  
Vol 5 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Ribelito F. Torregosa ◽  
Worsak Kanok-Nukulchai
Keyword(s):  
Genetic Algorithm ◽  
Steel Frames ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Weight Optimization ◽  
Discrete Variables ◽  
Design Problems ◽  
Plane Frames ◽  
Population Sizes ◽  
A New Technique

Genetic Algorithm (GA) is a new technique in optimization procedure that works best in design problems with discrete variables. It employs the survival of the fittest philosophy in determining the optimum combination. GA optimization procedure is applied to weight optimization of steel plane frames subjected to different load cases. Database of steel beam sizes is provided as the discrete variables. Both elitist and non-elitist search procedures are used to optimize the total weight of steel frames. Crossover types used are 20- and 50-percent uniform. Optimization result using population sizes 10, 20, and 40 are compared. Elitist search procedure showed superior results when compared to non-elitist for higher population sizes search because of its faster convergence rate. Performance of non-elitist is superior when using lesser population sizes. To examine the performance of genetic algorithms, case studies are conducted by varying material groups and the results are compared with the results from other optimization techniques. Genetic optimization showed superior results when compared to other techniques especially to problems with few material groupings.

scholarly journals Optimal Design of a Dragonfly-Inspired Compliant Joint for Camera Positioning System of Nanoindentation Tester Based on a Hybrid Integration of Jaya-ANFIS

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Ngoc Le Chau ◽  
Thanh-Phong Dao ◽  
Van Thanh Tien Nguyen
Keyword(s):  
Taguchi Method ◽  
Mean Squared Error ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Design Parameters ◽  
Positioning System ◽  
Jaya Algorithm ◽  
Inference System ◽  
Computing Technique ◽  
Compliant Joint

Camera positioning system is a critical member of a nanoindentation tester characterizing the mechanical properties such as hardness, creep, surface roughness, or elastic modulus of a material sample. This paper presents a design optimization for a dragonfly-inspired compliant joint. This joint is used to drive the camera positioning system. A new hybrid approach of Taguchi method, adaptive neuro-fuzzy inference system (ANFIS), and Jaya algorithm is developed to solve the multi-objective optimization problem. The Taguchi method is used to build the numerical data and to find the best membership functions for the ANFIS structure by minimizing the root mean squared error. Then, the weight factor of each objective function is determined by established equations well. Subsequently, a structure of ANFIS is developed to map the design parameters and responses. Sensitivity analysis of each controllable parameter is analyzed by the statistical method. Finally, Jaya algorithm is initialized to find the optimal solution. The results found that the optimal displacement, frequency, and stress are about 12581.11 μm, 67.76 Hz, and 333.68 MPa, respectively. The proposed hybrid optimization algorithm is a robust and effective optimizer and considered as soft computing technique for engineering optimization problems.

scholarly journals Application of Genetic Algorithm for Binary Optimization of Microstrip Antennas: A Review

2021 ◽  
Vol 5 (4) ◽  
pp. 315-333
Author(s):  
Jeevani W. Jayasinghe ◽  
Keyword(s):  
Genetic Algorithm ◽  
Performance Enhancement ◽  
Optimization Procedure ◽  
Microstrip Antennas ◽  
Optimization Techniques ◽  
Global Optimizer ◽  
Trial And Error Method ◽  
Miniature Antennas ◽  
Local Optimizer ◽  
Error Method

<abstract> <p>Researchers have proposed applying optimization techniques to improve performance of microstrip antennas (MSAs) in terms of bandwidth, radiation characteristics, polarization, directivity and size. The drawbacks of the conventional MSAs can be overcome by optimizing the antenna parameters while keeping a compact configuration. Applying a global optimizer is a better technique than using a local optimizer or a trial and error method for performance enhancement. This paper discusses genetic algorithm (GA) optimization of microstrip antennas presented by the antenna research community. The GA optimization procedure, antenna parameters optimized by using GA and the optimization objectives are presented by reviewing the literature. Further, evolution of GA in the field of MSAs and its significance are explored. Application of GA optimization to design broadband, multiband, high-directivity and miniature antennas is demonstrated with the support of several case studies giving an insight for further developments in the field.</p> </abstract>

Application of a Shell-Spring Model for the Optimization of Radial Tire Contour Using a Genetic Algorithm

2003 ◽  
Vol 31 (1) ◽  
pp. 39-63 ◽  
Author(s):  
G. Unnithan ◽  
R. KrishnaKumar ◽  
A. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
Element Model ◽  
Spring Model ◽  
New Approach ◽  
Shell Elements ◽  
Complex Optimization ◽  
Profile Optimization

Abstract Optimization gives a new facet to design and development of tires. A new approach to the tire profile optimization is proposed in this study. The optimization procedure is integrated with a simple shell-spring finite element model for faster evaluation. In the shell-spring model, the shell elements represent the tire carcass, whereas the tread is represented by the spring elements. This is applied for the optimization of the tire contour for better maneuverability. The genetic algorithm, an evolutionary optimization procedure that is robust and efficient in solving complex optimization problems, is chosen. A new tire contour is obtained that improves tire maneuverability by increasing the sidewall belt tension.

Grey-based Taguchi Method to Optimize the Multi-response Design of Product Form Design

2021 ◽  
Vol 20 (2) ◽  
pp. 136
Author(s):  
Sugoro Bhakti Sutono
Keyword(s):  
Taguchi Method ◽  
Product Design ◽  
Optimization Problems ◽  
Optimization Method ◽  
Product Form ◽  
Design Parameters ◽  
Optimal Product Design ◽  
Form Design ◽  
Response Optimization ◽  
Product Form Design

This paper presents a multi-response optimization method that uses the grey-based Taguchi method as the integrative product form design optimization method, and it serves as a tool for product form design to determine the optimal combination of design parameters in Kansei engineering (KE). This method is unique in that it combines the Taguchi method (TM) and grey relational analysis (GRA), allowing it to take advantage of the benefits of both methods. The TM is used to design experiments and generate combinative product form design samples which can be used to improve product quality. The GRA is applied to multi-response optimization problems. Factor effect analysis and analysis of variance (ANOVA) are used to determine which combinations of design parameters will result in the optimal product design. To demonstrate the applicability of the grey-based TM, a case study of a car form design is presented, and a confirmation test is performed to verify the performance of the optimal product design. The results show that the grey-based TM can deal with optimization problems with multiple Kansei responses and determine an optimal car form design that is representative of the consumers' perception in a systematic manner. The confirmation test results also show that the optimal product design generated by the grey-based TM can be used to improve the overall quality of a product form.

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