Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction
Genetic algorithm (GA) is widely accepted in energy systems optimization especially multi objective method. In multi objective method, a set of solutions called Pareto front is obtained. Due to random nature of GA, finding a unique and reproducible result is not an easy task for multi objective prob...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
Elsevier Ltd
2017
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/76115/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006867319&doi=10.1016%2fj.energy.2016.12.034&partnerID=40&md5=59b9ce75c0619e5f6c41a0327fd785ec |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.76115 |
---|---|
record_format |
eprints |
spelling |
my.utm.761152018-05-30T04:22:02Z http://eprints.utm.my/id/eprint/76115/ Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction Ganjehkaviri, A. Mohd. Jaafar, M. N. Hosseini, S. E. Barzegaravval, H. TJ Mechanical engineering and machinery Genetic algorithm (GA) is widely accepted in energy systems optimization especially multi objective method. In multi objective method, a set of solutions called Pareto front is obtained. Due to random nature of GA, finding a unique and reproducible result is not an easy task for multi objective problems. Here we discuss the solution uniqueness, accuracy, Pareto convergence, dimension reduction topics and provide quantitative methodologies for the mentioned parameters. Firstly, Pareto frontier goodness and solution accuracy is introduced. Then the convergence of Pareto front is discussed and the related methodology is developed. By comparing two different best points (optimum points) selection method, it is shown that multi objective methods can be reduced to single objective or lower dimensions in objective functions by using ratio method. Our results establish that our proposed method can indeed provide unique solution of satisfactory accuracy and convergence for a multi-objective optimization problem in energy systems. Elsevier Ltd 2017 Article PeerReviewed Ganjehkaviri, A. and Mohd. Jaafar, M. N. and Hosseini, S. E. and Barzegaravval, H. (2017) Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction. Energy, 119 . S125-S127. ISSN 0360-5442 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006867319&doi=10.1016%2fj.energy.2016.12.034&partnerID=40&md5=59b9ce75c0619e5f6c41a0327fd785ec |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
TJ Mechanical engineering and machinery |
spellingShingle |
TJ Mechanical engineering and machinery Ganjehkaviri, A. Mohd. Jaafar, M. N. Hosseini, S. E. Barzegaravval, H. Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
description |
Genetic algorithm (GA) is widely accepted in energy systems optimization especially multi objective method. In multi objective method, a set of solutions called Pareto front is obtained. Due to random nature of GA, finding a unique and reproducible result is not an easy task for multi objective problems. Here we discuss the solution uniqueness, accuracy, Pareto convergence, dimension reduction topics and provide quantitative methodologies for the mentioned parameters. Firstly, Pareto frontier goodness and solution accuracy is introduced. Then the convergence of Pareto front is discussed and the related methodology is developed. By comparing two different best points (optimum points) selection method, it is shown that multi objective methods can be reduced to single objective or lower dimensions in objective functions by using ratio method. Our results establish that our proposed method can indeed provide unique solution of satisfactory accuracy and convergence for a multi-objective optimization problem in energy systems. |
format |
Article |
author |
Ganjehkaviri, A. Mohd. Jaafar, M. N. Hosseini, S. E. Barzegaravval, H. |
author_facet |
Ganjehkaviri, A. Mohd. Jaafar, M. N. Hosseini, S. E. Barzegaravval, H. |
author_sort |
Ganjehkaviri, A. |
title |
Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
title_short |
Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
title_full |
Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
title_fullStr |
Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
title_full_unstemmed |
Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction |
title_sort |
genetic algorithm for optimization of energy systems: solution uniqueness, accuracy, pareto convergence and dimension reduction |
publisher |
Elsevier Ltd |
publishDate |
2017 |
url |
http://eprints.utm.my/id/eprint/76115/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006867319&doi=10.1016%2fj.energy.2016.12.034&partnerID=40&md5=59b9ce75c0619e5f6c41a0327fd785ec |
_version_ |
1643657221594152960 |
score |
13.18916 |