Optimization of Multistage Vapor Compression Systems Employing Genetic Algorithms

2005 ◽  
Author(s):  
A. C. West ◽  
S. A. Sherif
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Code ◽  
Coefficient Of Performance ◽  
Vapor Compression ◽  
Termination Criteria ◽  
Multistage System ◽  
New Generation ◽  
Survival Of The Fittest ◽  
Vapor Compression Systems

Genetic algorithms involve the coding of a solution into a binary string in the same manner that DNA is a biological coding. A population of binary strings are randomly created, evaluated, allowed to mate, and mutated to form a new generation of strings. There is a mating preference given to those strings which rate the highest to simulate the survival of the fittest theory that exists in nature. This process of evaluation, mating, and mutation is repeated until some termination criteria are met. A computer code was written to simulate the vapor compression systems and perpetuate the genetic algorithm. The genetic algorithm functioned adequately enough to provide general trends but it did not find a universal optimum. After numerous runs, the code produced data that suggest that systems which employ intercooler/flash tanks and operate at lower evaporating temperatures have a higher multistage effectiveness. Multistage effectiveness is a novel term defined as the ratio of the overall coefficient of performance (COP) of the multistage system and the combined coefficient of performance of a group of basic vapor compression systems with cooling capacities and evaporating temperatures that parallel the evaporators in the multistage system.

scholarly journals Novel Method Using Crossover (Genetic Algorithms) With Matrix Technique to Modifying Ciphering by Using Playfair

2013 ◽  
Vol 6 (3) ◽  
pp. 97-106
Author(s):  
Mohammed Sami Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
The Other ◽  
Plain Text ◽  
Cipher Text ◽  
One Step ◽  
Novel Method ◽  
Get Complex ◽  
New Generation

Two techniques combined with each other, to get complex one. One from technique of genetic algorithm (GA) and the other is PlayFair cipher method, in this research using one step of Genetic Algorithm (GA) which called Crossover to make offspring of two parents (characters) to get one or two new character by using these techniques then using PlayFair technique to cipher text (plain text). So the person who wants to break code, two techniques must know.This research is a novel method of ciphering by getting a new generation of offspring from two characters, when we give a new theory of symbols as mention in research.

scholarly journals Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Abid Hussain ◽  
Yousaf Shad Muhammad ◽  
M. Nauman Sajid ◽  
Ijaz Hussain ◽  
Alaa Mohamd Shoukry ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Crossover Operator ◽  
Mutation Operators ◽  
Path Representation ◽  
Hard Problems ◽  
The Traveling Salesman Problem ◽  
Survival Of The Fittest

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

Introducing Genetic Algorithm as an Intelligent Optimization Technique

2014 ◽  
Vol 568-570 ◽  
pp. 793-797 ◽  
Author(s):  
Ali Ashkzari ◽  
Aydin Azizi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Biological Evolution ◽  
Search Method ◽  
Intelligent Optimization ◽  
Problem Solving Strategies ◽  
Complex Optimization ◽  
Survival Of The Fittest

The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

scholarly journals State-of-the-Art Review on Relevance of Genetic Algorithm to Internet Web Search

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Kehinde Agbele ◽  
Ademola Adesina ◽  
Daniel Ekong ◽  
Oluwafemi Ayangbekun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Information Needs ◽  
Web Search ◽  
State Of The Art ◽  
Retrieval Systems ◽  
Online Databases ◽  
Information Retrieval Systems ◽  
Survival Of The Fittest

People use search engines to find information they desire with the aim that their information needs will be met. Information retrieval (IR) is a field that is concerned primarily with the searching and retrieving of information in the documents and also searching the search engine, online databases, and Internet. Genetic algorithms (GAs) are robust, efficient, and optimizated methods in a wide area of search problems motivated by Darwin’s principles of natural selection and survival of the fittest. This paper describes information retrieval systems (IRS) components. This paper looks at how GAs can be applied in the field of IR and specifically the relevance of genetic algorithms to internet web search. Finally, from the proposals surveyed it turns out that GA is applied to diverse problem fields of internet web search.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

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