scholarly journals Modelling of Liquid Flow control system Using Optimized Genetic Algorithm

2020 ◽  
Vol 8 (2) ◽  
pp. 565-582
Author(s):  
Pijush Dutta ◽  
Asok Kumar
Keyword(s):  
Genetic Algorithm ◽  
Liquid Flow ◽  
Linear Models ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Liquid Density ◽  
Flow Process ◽  
Computational Optimization ◽  
Non Linear ◽  
Selection Of

Estimation of a highly accurate model for  liquid flow process industry and control of the liquid flow rate from experimental data is an important task for engineers due to its non linear characteristics. Efficient optimization techniques are essential to accomplish this task.In most of the process control industry flowrate  depends upon a multiple number of parameters like sensor output,pipe diameter, liquid conductivity ,liquid viscosity & liquid density etc.In traditional optimization technique its very time consuming for manually control the parameters to obtain the optimial flowrate from the process.Hence the alternative approach , computational optimization process is utilized by using the different computational intelligence technique.In this paper three different selection of Genetic Algorithm is proposed & tested against the present liquid flow process.The proposed algorithm is developed based on the mimic  genetic evolution of species that allow the consecutive generations in  population to adopt their environment.Equations for Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) are being used as non-linear models and these models are optimized using the proposed different selection of Genetic optimization techniques. It can be observed that the among these three different selection of Genetic Algorithm ,Rank selected GA  is better than the other two selection (Tournament & Roulette wheel) in terms of the accuracy of final solutions, success rate, convergence speed, and stability.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

1999 ◽  
Author(s):  
Shapour Azar ◽  
Brian J. Reynolds ◽  
Sanjay Narayanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Set ◽  
Design Solution ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Continuous Variables ◽  
Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

scholarly journals Genetic Algorithm Applied to Multi-Criteria Selection of Thermal Insulation on Industrial Shed Roof

Buildings ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 238 ◽  
Author(s):  
Stamoulis ◽  
Santos ◽  
Lenz ◽  
Tusset
Keyword(s):  
Genetic Algorithm ◽  
Thermal Comfort ◽  
Thermal Insulation ◽  
Optimization Technique ◽  
Building Envelope ◽  
Expanded Polystyrene ◽  
Insulation Material ◽  
Clothing Insulation ◽  
Climate Conditions ◽  
Selection Of

The rational use of energy has motivated research on improving the energy efficiency of buildings, which are responsible for a large share of world consumption. A strategy to achieve this goal is the application of optimized thermal insulation on a building envelope to avoid thermal exchanges with the external environment, reducing the use of heating, ventilation and air-conditioning (HVAC) systems. In order to contribute to the best choice of insulation applied to an industrial shed roof, this study aims to provide an optimization tool to assist this process. Beyond the thermal comfort and cost of the insulation, some hygrothermic properties also have been analysed to obtain the best insulation option. To implement this optimization technique, several thermo-energetic simulations of an industrial shed were performed using the Domus software, applying 4 types of insulation material (polyurethane, expanded polystyrene, rockwool and glass wool) on the roof. Ten thicknesses ranging from 0.5 cm to 5 cm were considered, with the purpose of obtaining different thermal comfort indexes (PPD, predicted percentage dissatisfied). Posteriorly, the best insulation ranking has been obtained from the weights assigned to the parameters in the objective function, using the technique of the genetic algorithm (GA) applied to multi-criteria selection. The optimization results showed that polyurethane (PU) insulation, applied with a thickness of 1 cm was the best option for the roof, considering the building functional parameters, occupant metabolic activity, clothing insulation and climate conditions. On the other hand, when the Brazilian standard was utilized, rock wool (2 cm) was considered the best choice.

Passage rate of solids and fluids in the digestive tract of buffaloes, cattle and sheep: selection of non-linear model

1997 ◽  
Vol 64 (1) ◽  
pp. 63-69 ◽  
Author(s):  
A. Amici ◽  
S. Bartocci ◽  
S. Terramoccia ◽  
F. Martillotti
Keyword(s):  
Linear Models ◽  
Latin Square ◽  
Sums Of Squares ◽  
Passage Rate ◽  
Significant Difference ◽  
Age Dependent ◽  
Non Linear ◽  
Best Fitting ◽  
Kinetics Of ◽  
Selection Of

AbstractFive mathematical models were compared to select the most satisfactory model to describe digesta kinetics of solids and fluids in the gastrointestinal tract of buffaloes (Mediterranean bulls), cattle (Friesian bulls) and sheep (Delle Langhe rams) given food at maintenance level, according to a Latin-square arrangement for four consecutive periods of 21 days. Chromium mordanted alfalfa hay and cobalt-ethylenediamine tetraacetic acid were used as nonabsorbable markers and were administered through the rumen cannula in a single dose. Four different isonitrogenous diets (N × 6·25 = 140 g/kg dry matter) with different concentrate:forage ratios (12·5:87·5, 25:75, 37·5:62·5, 50:50) were used.Faecal chromium and cobalt concentration curves were fitted with five non-linear models: three gamma (G2, G3, G4) age-dependent one-compartment, one gamma age-dependent/age-independent two-compartment (G2G1) and one multicompartment (MC).Wilcoxon tests on residual sums of squares of the different models for solids showed that MC and G4 gave a better fit than G2G1, G2, G3 for all the data and within the species. The comparison of MC v. G4 did not show any significant difference (P > 0·05) for all the data computed together or within each species. Nevertheless, MC had a higher number of curves with lower residual sums of squares in comparison with G4 and was also able to produce estimates of digesta kinetics in the second compartment.The cobalt excretion curves for fluids, considering all the data, and only within sheep, showed G4 as the best fitting model. When G4 was compared with other models no significant differences were recorded either for cattle: G4 v. G2 (F = 0·6645), G4 v. G2G1 (P = 0·0620) and for buffalo: G4 v. G2 (P = 0·1575), G4 v.G3(P = 0·0796) and G4 v. G2G1 (P = 0·1641).It is concluded that the multicompartment model (MC) and G4 model were the best fits for solids and for fluids respectively.

A Crossover Improved Genetic Algorithm and Its Application in Non-Uniform Linear Antenna Arrays

2017 ◽  
Vol 16 (04) ◽  
pp. 1750027 ◽  
Author(s):  
Bhargav Appasani ◽  
Rahul Pelluri ◽  
Vijay Kumar Verma ◽  
Nisha Gupta
Keyword(s):  
Genetic Algorithm ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Beam Width ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Linear Antenna ◽  
Improved Genetic Algorithm ◽  
Element Array ◽  
Linear Antenna Arrays

Genetic Algorithm (GA) is a widely used optimization technique with multitudinous applications. Improving the performance of the GA would further augment its functionality. This paper presents a Crossover Improved GA (CIGA) that emulates the motion of fireflies employed in the Firefly Algorithm (FA). By employing this mimicked crossover operation, the overall performance of the GA is greatly enhanced. The CIGA is tested on 14 benchmark functions conjointly with the other existing optimization techniques to establish its superiority. Finally, the CIGA is applied to the practical optimization problem of synthesizing non-uniform linear antenna arrays with low side lobe levels (SLL) and low beam width, both requirements being incompatible. However, the proposed CIGA applied for the synthesis of a 12 element array yields an SLL of [Formula: see text]29.2[Formula: see text]dB and a reduced beam width of 19.1[Formula: see text].

Genetic-Algorithm-Based Optimization of Clustering in Mobile Ad Hoc Network

2016 ◽  
pp. 128-158 ◽  
Author(s):  
Koushik Majumder ◽  
Debashis De ◽  
Senjuti Kar ◽  
Rani Singh
Keyword(s):  
Genetic Algorithm ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Optimization Technique ◽  
Cluster Head ◽  
Optimization Techniques ◽  
Route Optimization ◽  
Future Research ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Mobile Ad hoc Networks (MANET) are wireless infrastructure less networks that are formed spontaneously and are highly dynamic in nature. Clustering is done in MANETs to address issues related to scalability, heterogeneity and to reduce network overhead. In clustering the entire network is divided into clusters or groups with one Cluster Head (CH) per cluster. The process of CH selection and route optimization is extremely crucial in clustering. Genetic Algorithm (GA) can be implemented to optimize the process of clustering in MANETs. GA is the most recently used advanced bio-inspired optimization technique which implements techniques of genetics like selection, crossover, mutation etc. to find out an improved solution to a problem similar to the next generation that inherits the positive traits and features of the previous one. In this chapter several genetic algorithm based optimization techniques for clustering has been discussed. A comparative analysis of the different approaches has also been presented. This chapter concludes with future research directions in this domain.

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.

Technique for optimizing of association rule mining by utilizing genetic algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Darshana H. Patel ◽  
Saurabh Shah ◽  
Avani Vasant
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Privacy Preservation ◽  
Personal Information ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Sensitive Information ◽  
Time Accuracy ◽  
Or Organization

With the advent of various technologies and digitization, popularity of the data mining has been increased for analysis and growth purpose in several fields. However, such pattern discovery by data mining also discloses personal information of an individual or organization. In today’s world, people are very much concerned about their sensitive information which they don’t want to share. Thus, it is very much required to protect the private data. This paper focuses on preserving the sensitive information as well as maintaining the efficiency which gets affected due to privacy preservation. Privacy is preserved by anonymization and efficiency is improved by optimization techniques as now days several advanced optimization techniques are used to solve the various problems of different areas. Furthermore, privacy preserving association classification has been implemented utilizing various datasets considering the accuracy parameter and it has been concluded that as privacy increases, accuracy gets degraded due to data transformation. Hence, optimization techniques are applied to improve the accuracy. In addition, comparison with the existing optimization technique namely particle swarm optimization, Cuckoo search and animal migration optimization has been carried out with the proposed approach specifically genetic algorithm for optimizing association rules.It has been concluded that the proposed approach requires more execution time about 20-80 milliseconds depending on the dataset but at the same time accuracy is improved by 5-6 % as compared to the existing approaches.

scholarly journals Optimizing Software Modularity with Minimum Possible Variations

2018 ◽  
Vol 29 (1) ◽  
pp. 1135-1150
Author(s):  
Amarjeet Prajapati ◽  
Jitender Kumar Chhabra
Keyword(s):  
Genetic Algorithm ◽  
Software Maintenance ◽  
Fitness Function ◽  
Optimization Technique ◽  
Quality Criteria ◽  
Optimization Techniques ◽  
Software Systems ◽  
Design Quality ◽  
Early Stages ◽  
Software Modularity

Abstract Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost.

Sign in / Sign up

Export Citation Format

Share Document