Dynamical memetization in coral reef optimization algorithms for optimal time series approximation

The huge amount of data chronologically collected in short periods of time by different devices and technologies is an important challenge in the analysis of times series. This problem has produced the development of new automatic techniques to reduce the number of points in the resulting time serie...

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Veröffentlicht in:Progress in artificial intelligence Jg. 8; H. 2; S. 253 - 262
Hauptverfasser: Durán-Rosal, Antonio M., Gutiérrez, Pedro A., Salcedo-Sanz, Sancho, Hervás-Martínez, César
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Artificial Intelligence
Cardiology
Computational Intelligence
Computer Imaging
Computer Science
Control
Coral reefs
Data Mining and Knowledge Discovery
Mathematical analysis
Mechatronics
Natural Language Processing (NLP)
Oceanography
Optimization
Pattern Recognition and Graphics
Regular Paper
Robotics
Time series
Vision
Coral reefs optimization
Memetic algorithms
Segmentation
Time series size reduction
Dynamic strategy
ISSN:2192-6352, 2192-6360
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Zusammenfassung:The huge amount of data chronologically collected in short periods of time by different devices and technologies is an important challenge in the analysis of times series. This problem has produced the development of new automatic techniques to reduce the number of points in the resulting time series, in order to facilitate their processing and analysis. In this paper, we propose a new modification of a coral reefs optimization algorithm (CRO) to tackle the problem of reducing the size of the time series minimizing the approximation error. The modification includes a memetization procedure (hybridization with a local search procedure) of the standard algorithm to improve its quality when finding a promising solution in a given searching area. The memetization process is applied to the worse individuals of the algorithm at the beginning, and only to the best ones at the end of the algorithm’s convergence, resulting in a dynamical search approach called dynamic memetic CRO (DMCRO). The proposed DMCRO performance is compared in this paper against other state-of-the-art CRO algorithms, such as the standard one, its statistically driven version (SCRO) and two different hybrid versions (HCRO and HSCRO, respectively), and the standard memetic version (MCRO). All the algorithms compared have been tested in 15 time series approximation, collected from different sources, including financial problems, oceanography data, and cardiology signals, among others, showing that the best results are obtained by DMCRO.
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ISSN:2192-6352
2192-6360
DOI:10.1007/s13748-019-00176-0