An optimization algorithm inspired by musical composition in constrained optimization problems

Many real-world problems can be expressed as an instance of the constrained nonlinear optimization problem (CNOP). This problem has a set of constraints specifies the feasible solution space. In the last years several algorithms have been proposed and developed for tackling CNOP. In this paper, we p...

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Main Authors: Mora Guti?rrez, Roman Anselmo, Rinc?n Garc?a, Eric Alfredo, Ram?rez Rodr?guez, Javier, Ponsich, Antonin, Herrera Alc?ntara, Oscar, Lara Vel?zquez, Pedro
Format: Artículo
Language: Español
Published: 2015
Online Access: http://revistas.ucr.ac.cr/index.php/matematica/article/view/11658
http://hdl.handle.net/10669/13034
id RepoKERWA13034
recordtype dspace
spelling RepoKERWA130342017-08-08T18:50:27Z An optimization algorithm inspired by musical composition in constrained optimization problems Un algoritmo de optimizaci?n inspirado en composici?n musical para el problema de optimizaci?n con restricciones Mora Guti?rrez, Roman Anselmo Rinc?n Garc?a, Eric Alfredo Ram?rez Rodr?guez, Javier Ponsich, Antonin Herrera Alc?ntara, Oscar Lara Vel?zquez, Pedro Many real-world problems can be expressed as an instance of the constrained nonlinear optimization problem (CNOP). This problem has a set of constraints specifies the feasible solution space. In the last years several algorithms have been proposed and developed for tackling CNOP. In this paper, we present a cultural algorithm for constrained optimization, which is an adaptation of ?Musical Composition Method? or MCM, which was proposed in [33] by Mora et al. We evaluated and analyzed the performance of MCM on five test cases benchmark of the CNOP. Numerical results were compared to evolutionary algorithm based on homomorphous mapping [23], Artificial Immune System [9] and anti-culture population algorithm [39]. The experimental results demonstrate that MCM significantly improves the global performances of the other tested metaheuristics on same of benchmark functions. Muchos de los problemas reales se pueden expresar como una instancia del problema de optimizaci?n no lineal con restricciones (CNOP). Este problema tiene un conjunto de restricciones, el cual especifica el espacio de soluciones factibles. En los ?ltimos a?os se han propuesto y desarrollado varios algoritmos para resolver el CNOP. En este trabajo, se presenta un algoritmo cultural para optimizaci?n con restricciones, el cual es una adaptaci?n del ? M?todo de Composici?n Musical? o MCM, propuesto en [33] por Mora et al., para resolver instancias del CNOP. La adaptaci?n propuesta del MCM se aplic? a cinco instancias de prueba del CNOP a fin de evaluar y analizar su comportamiento. Los resultados experimentales del MCM se compararon con los resultados obtenidos por algoritmo evolutivo basado en homomorfismo [23] , Sistema Inmune Artificial [9] y el algoritmo de anti-cultural [39]. Los resultados experimentales muestran que el MCM genera resultados significativamente mejores que los obtenidos por las otras metaheur?sticas probadas en algunos de los problemas de referencia. 2015-05-19T19:09:25Z 2015-05-19T19:09:25Z 2013-08-29 00:00:00 2015-05-19T19:09:25Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://revistas.ucr.ac.cr/index.php/matematica/article/view/11658 http://hdl.handle.net/10669/13034 10.15517/rmta.v20i2.11658 es Revista de Matem?tica: Teor?a y Aplicaciones Vol. 20 N?m. 2 183-202 application/pdf
institution Universidad de Costa Rica
collection Repositorio KERWA
language Español
description Many real-world problems can be expressed as an instance of the constrained nonlinear optimization problem (CNOP). This problem has a set of constraints specifies the feasible solution space. In the last years several algorithms have been proposed and developed for tackling CNOP. In this paper, we present a cultural algorithm for constrained optimization, which is an adaptation of ?Musical Composition Method? or MCM, which was proposed in [33] by Mora et al. We evaluated and analyzed the performance of MCM on five test cases benchmark of the CNOP. Numerical results were compared to evolutionary algorithm based on homomorphous mapping [23], Artificial Immune System [9] and anti-culture population algorithm [39]. The experimental results demonstrate that MCM significantly improves the global performances of the other tested metaheuristics on same of benchmark functions.
format Artículo
author Mora Guti?rrez, Roman Anselmo
Rinc?n Garc?a, Eric Alfredo
Ram?rez Rodr?guez, Javier
Ponsich, Antonin
Herrera Alc?ntara, Oscar
Lara Vel?zquez, Pedro
spellingShingle Mora Guti?rrez, Roman Anselmo
Rinc?n Garc?a, Eric Alfredo
Ram?rez Rodr?guez, Javier
Ponsich, Antonin
Herrera Alc?ntara, Oscar
Lara Vel?zquez, Pedro
An optimization algorithm inspired by musical composition in constrained optimization problems
author_sort Mora Guti?rrez, Roman Anselmo
title An optimization algorithm inspired by musical composition in constrained optimization problems
title_short An optimization algorithm inspired by musical composition in constrained optimization problems
title_full An optimization algorithm inspired by musical composition in constrained optimization problems
title_fullStr An optimization algorithm inspired by musical composition in constrained optimization problems
title_full_unstemmed An optimization algorithm inspired by musical composition in constrained optimization problems
title_sort optimization algorithm inspired by musical composition in constrained optimization problems
publishDate 2015
url http://revistas.ucr.ac.cr/index.php/matematica/article/view/11658
http://hdl.handle.net/10669/13034
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