Fuzzy bi-objective particle swarm optimization for next release poblem

In search-based software engineering (SBSE), software engineers usually have to select one among many quasi-optimal solutions with different values for the objectives of interest for a particular problem domain. Because of this, a metaheuristic algorithm is needed to explore a larger extension of th...

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Autores principales: Casanova Pietroboni, Carlos Antonio, Rottoli, Giovanni Daián, Schab, Esteban Alejandro, Bracco, Luciano Joaquín, Pereyra Rausch, Fernando Nahuel, De Battista, Anabella Cecilia
Formato: Documento de conferencia publishedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
Software engineering
Multi-objective optimization
Particle swarm optimization
Next release problem
Fuzzy logic
Acceso en línea:http://hdl.handle.net/20.500.12272/4397
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-4397
record_format dspace
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Software engineering
Multi-objective optimization
Particle swarm optimization
Next release problem
Fuzzy logic
spellingShingle Software engineering
Multi-objective optimization
Particle swarm optimization
Next release problem
Fuzzy logic
Casanova Pietroboni, Carlos Antonio
Rottoli, Giovanni Daián
Schab, Esteban Alejandro
Bracco, Luciano Joaquín
Pereyra Rausch, Fernando Nahuel
De Battista, Anabella Cecilia
Fuzzy bi-objective particle swarm optimization for next release poblem
topic_facet Software engineering
Multi-objective optimization
Particle swarm optimization
Next release problem
Fuzzy logic
description In search-based software engineering (SBSE), software engineers usually have to select one among many quasi-optimal solutions with different values for the objectives of interest for a particular problem domain. Because of this, a metaheuristic algorithm is needed to explore a larger extension of the Pareto optimal front to provide a bigger set of possible solutions. In this regard the Fuzzy Multi-Objective Particle Swarm Optimization (FMOPSO), a novel a posteriori algorithm, is proposed in this paper and compared with other state-of-the-art algorithms. The results show that FMOPSO is adequate for finding very detailed Pareto Fronts.
format Documento de conferencia
publishedVersion
author Casanova Pietroboni, Carlos Antonio
Rottoli, Giovanni Daián
Schab, Esteban Alejandro
Bracco, Luciano Joaquín
Pereyra Rausch, Fernando Nahuel
De Battista, Anabella Cecilia
author_facet Casanova Pietroboni, Carlos Antonio
Rottoli, Giovanni Daián
Schab, Esteban Alejandro
Bracco, Luciano Joaquín
Pereyra Rausch, Fernando Nahuel
De Battista, Anabella Cecilia
author_sort Casanova Pietroboni, Carlos Antonio
title Fuzzy bi-objective particle swarm optimization for next release poblem
title_short Fuzzy bi-objective particle swarm optimization for next release poblem
title_full Fuzzy bi-objective particle swarm optimization for next release poblem
title_fullStr Fuzzy bi-objective particle swarm optimization for next release poblem
title_full_unstemmed Fuzzy bi-objective particle swarm optimization for next release poblem
title_sort fuzzy bi-objective particle swarm optimization for next release poblem
publishDate 2020
url http://hdl.handle.net/20.500.12272/4397
work_keys_str_mv AT casanovapietrobonicarlosantonio fuzzybiobjectiveparticleswarmoptimizationfornextreleasepoblem
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