scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

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.     

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.

PERFORMANCE OF A REAL CODED GENETIC ALGORITHM FOR THE CALIBRATION OF SCALAR CONSERVATION LAWS

2016 ◽  
Vol 58 (1) ◽  
pp. 51-77 ◽  
Author(s):  
S. BERRES ◽  
A. CORONEL ◽  
R. LAGOS ◽  
M. SEPÚLVEDA
Keyword(s):  
Genetic Algorithm ◽  
Conservation Laws ◽  
Direct Problem ◽  
Optimization Technique ◽  
Identification Problem ◽  
Optimization Techniques ◽  
Finite Volume Scheme ◽  
Scalar Conservation Laws ◽  
Real Coded Genetic Algorithm ◽  
Scalar Conservation

This paper deals with the flux identification problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of the direct problem with observed profiles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.

scholarly journals An Isolated Traffic Signal Design using a GA-based Optimization Technique

2020 ◽  
Vol 8 (5) ◽  
pp. 2598-2604
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Optimization Technique ◽  
Traffic Signal ◽  
Signal Design ◽  
Next Generation ◽  
Two Phase ◽  
Local Optima ◽  
Variable Approach ◽  
Fitness Index

The Genetic Algorithm (GA) approach is an evolutionary optimization technique, which is developed based on the fundamental theories of natural selection and evolution. The present study focuses on the design of an isolated traffic signal for a two-phase intersection using the conventional method and GA-based optimization technique for unsaturated traffic flow conditions. The methodology of the study includes formulation of an objective function and the constraints, formulation of the constraint violation coefficient, formulation of the modified objective function, formulation of the fitness index (fi), and the GA operations to determine the best green signal timings. The intrinsic nature of Genetic Algorithms in performing elitism, which assures to carry forward the best solution identified in each generation to the next generation. An example problem was solved to demonstrate elitism using binary genetic algorithm with a single variable approach. In the GA operation, parent strings/chromosomes are selected and the crossover is performed along with mutation to form the new offspring’s. Mutation helps in avoiding the convergence of the solution to local optima. In this operation Fitness Index (FI) values of each strings/chromosomes are used as a measure to identify the parent strings to perform GA operations for the next generation. The results of the study indicate that the proposed technique can be used to optimize the signal timings of an isolated traffic signal, this will influence on reducing the delays at the junctions.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2012 ◽  
pp. 643-662
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2011 ◽  
pp. 212-231 ◽  
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

scholarly journals RCGAToolbox: A real-coded genetic algorithm software for parameter estimation of kinetic models

2021 ◽  
Author(s):  
Kazuhiro Maeda ◽  
Fred C. Boogerd ◽  
Hiroyuki Kurata
Keyword(s):  
Parameter Estimation ◽  
User Interfaces ◽  
Kinetic Models ◽  
Biochemical Networks ◽  
Generation Gap ◽  
Supplementary Information ◽  
Real Coded Genetic Algorithm ◽  
Supplementary Material ◽  
Real Coded Genetic Algorithms ◽  
User Friendly

AbstractSummaryKinetic modeling is essential in understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in the development of kinetic models. We present RCGAToolbox, software for real-coded genetic algorithms (RCGAs), which accelerates the parameter estimation of kinetic models. RCGAToolbox provides two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and real-coded ensemble crossover star with just generation gap (REXstar/JGG), using the stochastic ranking method. The RCGAToolbox also provides user-friendly graphical user interfaces.Availability and implementationRCGAToolbox is available from https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, with application examples. The user guide is provided in the Supplementary Material. RCGAToolbox runs on MATLAB in Windows, Linux, and [email protected] informationSupplementary Material is available at Bioinformatics online.

scholarly journals Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Optimization Technique ◽  
Electric Power System ◽  
Fuel Cost ◽  
Renewable Source ◽  
Wind Generators ◽  
Real Coded Genetic Algorithm ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

scholarly journals Tuning Genetic Algorithm Parameters to Improve Convergence Time

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Convergence Time ◽  
Generation Gap ◽  
Genetic Operators ◽  
Time Saving ◽  
Fermentation Processes ◽  
Highly Nonlinear ◽  
Crossover And Mutation

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Sign in / Sign up

Export Citation Format

Share Document