SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM

A system of nonlinear equations is a collection of several interrelated non-linear equations. Currently, systems of nonlinear equations are used not only on crisp but also on fuzzy numbers. A fuzzy number is an ordered pair function that has a degree of membership [0,1]. Meanwhile, a fully fuzzy sys...

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Main Authors: Fatimatuzzahra Fatimatuzzahra, Aang Nuryaman, La Zakaria, Agus Sutrisno
Format: Article
Language:English
Published: Universitas Pattimura 2025-04-01
Series:Barekeng
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Online Access:https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15419
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author Fatimatuzzahra Fatimatuzzahra
Aang Nuryaman
La Zakaria
Agus Sutrisno
author_facet Fatimatuzzahra Fatimatuzzahra
Aang Nuryaman
La Zakaria
Agus Sutrisno
author_sort Fatimatuzzahra Fatimatuzzahra
collection DOAJ
description A system of nonlinear equations is a collection of several interrelated non-linear equations. Currently, systems of nonlinear equations are used not only on crisp but also on fuzzy numbers. A fuzzy number is an ordered pair function that has a degree of membership [0,1]. Meanwhile, a fully fuzzy system of equations is a system of equations that applies fuzzy number arithmetic operations. The solution of non-linear equation systems is usually complicated to solve analytically, so numerical methods are used as an alternative to solve these problems. In this research, the steps to find the solution of nonlinear fully fuzzy equation systems using genetic algorithms are studied, which in the solution process is based on the theory of evolution and natural selection. The solution steps taken are first converting the fully fuzzy system of equations into a system of crisp equations, next constructing the system of strict equations as a multi-objective optimization problem, and lastly solving the optimization problem using a genetic algorithm which includes initialization, evaluation, selection, crossover, and mutation. As illustrations, several cases of nonlinear fully fuzzy and dual fully fuzzy systems of equations on triangular fuzzy numbers and trapezoidal fuzzy numbers are given. The approximate solutions obtained using genetic algorithms produce solutions that are close to their analytic solutions.
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institution Kabale University
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publishDate 2025-04-01
publisher Universitas Pattimura
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spelling doaj-art-8c7ac5dad3ca4e15bea8cc416e5f08272025-08-20T04:01:48ZengUniversitas PattimuraBarekeng1978-72272615-30172025-04-011921169117810.30598/barekengvol19iss2pp1169-117815419SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHMFatimatuzzahra Fatimatuzzahra0Aang Nuryaman1La Zakaria2Agus Sutrisno3Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, IndonesiaA system of nonlinear equations is a collection of several interrelated non-linear equations. Currently, systems of nonlinear equations are used not only on crisp but also on fuzzy numbers. A fuzzy number is an ordered pair function that has a degree of membership [0,1]. Meanwhile, a fully fuzzy system of equations is a system of equations that applies fuzzy number arithmetic operations. The solution of non-linear equation systems is usually complicated to solve analytically, so numerical methods are used as an alternative to solve these problems. In this research, the steps to find the solution of nonlinear fully fuzzy equation systems using genetic algorithms are studied, which in the solution process is based on the theory of evolution and natural selection. The solution steps taken are first converting the fully fuzzy system of equations into a system of crisp equations, next constructing the system of strict equations as a multi-objective optimization problem, and lastly solving the optimization problem using a genetic algorithm which includes initialization, evaluation, selection, crossover, and mutation. As illustrations, several cases of nonlinear fully fuzzy and dual fully fuzzy systems of equations on triangular fuzzy numbers and trapezoidal fuzzy numbers are given. The approximate solutions obtained using genetic algorithms produce solutions that are close to their analytic solutions.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15419fully fuzzy nonlinear equation systemsfuzzy numbersgenetic algorithmoptimization problems
spellingShingle Fatimatuzzahra Fatimatuzzahra
Aang Nuryaman
La Zakaria
Agus Sutrisno
SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
Barekeng
fully fuzzy nonlinear equation systems
fuzzy numbers
genetic algorithm
optimization problems
title SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
title_full SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
title_fullStr SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
title_full_unstemmed SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
title_short SOLUTION OF FULLY FUZZY NONLINEAR EQUATION SYSTEMS USING GENETIC ALGORITHM
title_sort solution of fully fuzzy nonlinear equation systems using genetic algorithm
topic fully fuzzy nonlinear equation systems
fuzzy numbers
genetic algorithm
optimization problems
url https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15419
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AT aangnuryaman solutionoffullyfuzzynonlinearequationsystemsusinggeneticalgorithm
AT lazakaria solutionoffullyfuzzynonlinearequationsystemsusinggeneticalgorithm
AT agussutrisno solutionoffullyfuzzynonlinearequationsystemsusinggeneticalgorithm