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 A Multiple Mitosis Genetic Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 252
Author(s):  
K. Kamil ◽  
K.H Chong ◽  
H. Hashim ◽  
S.A. Shaaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Total Population ◽  
Global Optimum ◽  
Natural Process ◽  
Local Optimum ◽  
Simple Genetic Algorithm ◽  
Cell Reproduction ◽  
Solve Optimization Problem ◽  
High Diversity

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

Route Optimization of DGT Based on Improved Genetic Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 845-848
Author(s):  
Tong Jie Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Route Optimization ◽  
Path Optimization ◽  
Initial Population ◽  
Local Optimum ◽  
Search Efficiency ◽  
Improved Genetic Algorithm ◽  
Adaptive Adjustment ◽  
Dynamic Operator

An improved genetic algorithm for route optimization in DGT is proposed in this paper. In which, method of initial population, cross and mutation are improved to make it more suitable for DGT. It uses a dynamic operator to realize the adaptive adjustment of the parameters. The experimental results show that the improved algorithm overcomes the shortcomings of local optimum and "premature convergence" and improves the search efficiency and adaptability. The proposed algorithm can effectively solve the path optimization problem in DGT in time.

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

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.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

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