M
Mariana Macedo
Researcher at University of Exeter
Publications - 35
Citations - 325
Mariana Macedo is an academic researcher from University of Exeter. The author has contributed to research in topics: Swarm behaviour & Swarm intelligence. The author has an hindex of 7, co-authored 29 publications receiving 173 citations. Previous affiliations of Mariana Macedo include University of Porto & Universidade de Pernambuco.
Papers
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Journal ArticleDOI
Swarm intelligence for clustering — A systematic review with new perspectives on data mining
Elliackin M. N. Figueiredo,Mariana Macedo,Hugo Siqueira,Clodomir J. Santana,Anu Gokhale,Carmelo J. A. Bastos-Filho +5 more
TL;DR: A systematic mapping review on recent investigations of swarm-inspired algorithms to tackle clustering problems and provides an overview of how to apply the swarm methods together with a critical analysis of the current and future perspectives in the field.
Journal ArticleDOI
A novel binary artificial bee colony algorithm
TL;DR: A novel artificial bee colony algorithm, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees’ phase, which outperformed not only the binary-based ABCs but also the other binary swarm-based and evolutionary-based optimizers.
Journal ArticleDOI
Simplified binary cat swarm optimization
Hugo Siqueira,Clodomir J. Santana,Mariana Macedo,Elliackin M. N. Figueiredo,Anuradha Gokhale,Carmelo J. A. Bastos-Filho +5 more
TL;DR: This new version, named Simplified Binary CSO (SBCSO) which features a new position update rule for the tracing mode, demonstrates improved performance, and reduced computational cost when compared to previous CSO versions, including the BBCSO.
Proceedings ArticleDOI
Application of PSO-based clustering algorithms on educational databases
Pedro Santos,Mariana Macedo,Elliackin M. N. Figueiredo,Clodomir J. Santana,Fabiana Soares,Hugo Siqueira,Alexandre Magno Andrade Maciel,Anuradha Gokhale,Carmelo J. A. Bastos-Filho +8 more
TL;DR: The results show that the PSOClustering algorithm can achieve the best performance when compared to the other PSO-based algorithms and the standard K-means algorithm.
Journal ArticleDOI
Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods
Hugo Siqueira,Mariana Macedo,Yara de Souza Tadano,Thiago Antonini Alves,Sergio Luiz Stevan,Domingos S. Oliveira,Manoel Henrique da Nóbrega Marinho,Paulo S. G. de Mattos Neto,João Fausto Lorenzato de Oliveira,Ivette Luna,Marcos de Almeida Leone Filho,Leonie Asfora Sarubbo,Attilio Converti +12 more
TL;DR: The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.