Optimization of Quadratic Curve Fitting from Data Points Using Real Coded Genetic Algorithm

2021 ◽  
pp. 419-428
Author(s):  
Banashree Mandal
Keyword(s):  
Genetic Algorithm ◽  
Curve Fitting ◽  
Quadratic Curve ◽  
Real Coded Genetic Algorithm ◽  
Data Points

A GA Based Measurement Method for Discontinuous Curve Fitting

2011 ◽  
Vol 460-461 ◽  
pp. 106-110 ◽  
Author(s):  
Hung Jen Chen ◽  
Hao En Chueh ◽  
Deng Yang Huang
Keyword(s):  
Genetic Algorithm ◽  
Curve Fitting ◽  
Measurement Method ◽  
Optimization Problem ◽  
Local Optimum ◽  
Suitable Function ◽  
Data Points ◽  
Best Fitting ◽  
Fitting Problem

Discontinuous curve fitting is the task of finding a suitable function which is best fitting to a given set of data points. Once the expression forms of function and of error are determined, curve fitting is regarded as an optimization problem. In this paper, a measurement method is proposed to optimize curve fitting problem. The proposed method is based on genetic algorithm, namely GAOCF (GA based Optimizer for Curve Fitting). The main advantage is that the proposed GA based method can skip the local optimum solutions, and find out better solution in a huge searching space. Consequently, some arc data will be used to verify the feasibility and effectiveness of the GA based measurement method proposed in this paper.

Applying Real-Valued Genetic Algorithm on Curve Fitting Problem

2010 ◽  
Vol 121-122 ◽  
pp. 183-187
Author(s):  
Hung Jen Chen ◽  
Hao En Chueh
Keyword(s):  
Genetic Algorithm ◽  
Curve Fitting ◽  
Computational Time ◽  
Technical Problems ◽  
Finite Set ◽  
Data Points ◽  
Optimization Efficiency ◽  
Computational Processes ◽  
Fitting Problem ◽  
Fitting Accuracy

Curve fitting refers to the process of finding an appropriate function that fits a finite set of data points. Representing a set of data points by a function is quite beneficial in data analysis and reapplication, and this technique is often used in engineering and technical problems. Fitting accuracy and computational time are usually the most crucial factors to be taken care of in curve fitting problems. Previous researchers have demonstrated that genetic algorithms can effectively solve curve fitting problems, but the difficulty of parameter coding is also widely encountered in computational processes. Hence, this study addresses on applying real-valued genetic algorithm to deal with curve fitting problems. Detailed discussion is made on the optimization efficiency among various data, and finally, some key parameters to curve fitting results are found and presented.

scholarly journals Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

2019 ◽  
Vol 9 (11) ◽  
pp. 2336 ◽  
Author(s):  
Jose Edgar Lara-Ramirez ◽  
Carlos Hugo Garcia-Capulin ◽  
Maria de Jesus Estudillo-Ayala ◽  
Juan Gabriel Avina-Cervantes ◽  
Raul Enrique Sanchez-Yanez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Curve Fitting ◽  
Fitness Function ◽  
Scattered Data ◽  
Model Parameters ◽  
B Spline ◽  
B Splines ◽  
Scattered Data Fitting ◽  
Data Points ◽  
Hierarchical Genetic Algorithm

Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansur Mohammed Ali Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M. A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

AbstractThe optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

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