Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic

Researchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA) is among these algorithms. This algorithm is inspired by Newton's l...

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Main Authors: Omid Mokhlesi, Seyed Hamid Zahiri, Naser Mehrshad, Seyed Mohammad Razavi
Format: Article
Language:English
Published: Najafabad Branch, Islamic Azad University 2013-04-01
Series:Journal of Intelligent Procedures in Electrical Technology
Subjects:
Online Access:http://jipet.iaun.ac.ir/pdf_4168_928ea1d91db433e1fa4e6225d395cd0d.html
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spelling doaj-5c8468b8be0649cc8a116d8db909b06d2020-11-24T22:37:39ZengNajafabad Branch, Islamic Azad UniversityJournal of Intelligent Procedures in Electrical Technology2322-38712345-55942013-04-014144152Improved Gravitational Search Algorithm (GSA) Using Fuzzy LogicOmid Mokhlesi0Seyed Hamid Zahiri1Naser Mehrshad2Seyed Mohammad Razavi3khorasan Institute for Higher EducationUniversity of BirjandUniversity of BirjandUniversity of BirjandResearchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA) is among these algorithms. This algorithm is inspired by Newton's laws of physics and gravitational attraction. Random masses are agents who have searched for the space. This paper presents a new Fuzzy Population GSA model called FPGSA. The proposed method is a combination of parametric fuzzy controller and gravitational search algorithm. The space being searched using this combined reasonable and accurate method. In the collective intelligence algorithms, population size influences the final answer so that for a large population, a better response is obtained but the algorithm execution time is longer. To overcome this problem, a new parameter called the dispersion coefficient is added to the algorithm. Implementation results show that by controlling this factor, system performance can be improved.http://jipet.iaun.ac.ir/pdf_4168_928ea1d91db433e1fa4e6225d395cd0d.htmlParametric fuzzy controllerGravitational search algorithm (GSA)Scattering coefficientFuzzy Population model
collection DOAJ
language English
format Article
sources DOAJ
author Omid Mokhlesi
Seyed Hamid Zahiri
Naser Mehrshad
Seyed Mohammad Razavi
spellingShingle Omid Mokhlesi
Seyed Hamid Zahiri
Naser Mehrshad
Seyed Mohammad Razavi
Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
Journal of Intelligent Procedures in Electrical Technology
Parametric fuzzy controller
Gravitational search algorithm (GSA)
Scattering coefficient
Fuzzy Population model
author_facet Omid Mokhlesi
Seyed Hamid Zahiri
Naser Mehrshad
Seyed Mohammad Razavi
author_sort Omid Mokhlesi
title Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
title_short Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
title_full Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
title_fullStr Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
title_full_unstemmed Improved Gravitational Search Algorithm (GSA) Using Fuzzy Logic
title_sort improved gravitational search algorithm (gsa) using fuzzy logic
publisher Najafabad Branch, Islamic Azad University
series Journal of Intelligent Procedures in Electrical Technology
issn 2322-3871
2345-5594
publishDate 2013-04-01
description Researchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA) is among these algorithms. This algorithm is inspired by Newton's laws of physics and gravitational attraction. Random masses are agents who have searched for the space. This paper presents a new Fuzzy Population GSA model called FPGSA. The proposed method is a combination of parametric fuzzy controller and gravitational search algorithm. The space being searched using this combined reasonable and accurate method. In the collective intelligence algorithms, population size influences the final answer so that for a large population, a better response is obtained but the algorithm execution time is longer. To overcome this problem, a new parameter called the dispersion coefficient is added to the algorithm. Implementation results show that by controlling this factor, system performance can be improved.
topic Parametric fuzzy controller
Gravitational search algorithm (GSA)
Scattering coefficient
Fuzzy Population model
url http://jipet.iaun.ac.ir/pdf_4168_928ea1d91db433e1fa4e6225d395cd0d.html
work_keys_str_mv AT omidmokhlesi improvedgravitationalsearchalgorithmgsausingfuzzylogic
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AT seyedmohammadrazavi improvedgravitationalsearchalgorithmgsausingfuzzylogic
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