Parallel population-based algorithm portfolios: An empirical study
Although many algorithms have been proposed, no single algorithm is better than others on all types of problems. Therefore, the search characteristics of different algorithms that show complementary behavior can be combined through portfolio structures to improve the performance on a wider set of pr...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 247; s. 115 - 125 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
19.07.2017
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Although many algorithms have been proposed, no single algorithm is better than others on all types of problems. Therefore, the search characteristics of different algorithms that show complementary behavior can be combined through portfolio structures to improve the performance on a wider set of problems. In this work, a portfolio of the Artificial Bee Colony, Differential Evolution and Particle Swarm Optimization algorithms was constructed and the first parallel implementation of the population-based algorithm portfolio was carried out by means of a Message Passing Interface environment. The parallel implementation of an algorithm or a portfolio can be performed by different models such as master-slave, coarse-grained or a hybrid of both, as used in this study. Hence, the efficiency and running time of various parallel implementations with different parameter values and combinations were investigated on benchmark problems. The performance of the parallel portfolio was compared to those of the single constituent algorithms. The results showed that the proposed models reduced the running time and the portfolio delivered a robust performance compared to each constituent algorithm. It is observed that the speedup gained over the sequential counterpart changed significantly depending on the structure of the portfolio. The portfolio is also applied to a training of neural networks which has been used for time series prediction. Result demonstrate that, portfolio is able to produce good prediction accuracy. |
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| AbstractList | Although many algorithms have been proposed, no single algorithm is better than others on all types of problems. Therefore, the search characteristics of different algorithms that show complementary behavior can be combined through portfolio structures to improve the performance on a wider set of problems. In this work, a portfolio of the Artificial Bee Colony, Differential Evolution and Particle Swarm Optimization algorithms was constructed and the first parallel implementation of the population-based algorithm portfolio was carried out by means of a Message Passing Interface environment. The parallel implementation of an algorithm or a portfolio can be performed by different models such as master-slave, coarse-grained or a hybrid of both, as used in this study. Hence, the efficiency and running time of various parallel implementations with different parameter values and combinations were investigated on benchmark problems. The performance of the parallel portfolio was compared to those of the single constituent algorithms. The results showed that the proposed models reduced the running time and the portfolio delivered a robust performance compared to each constituent algorithm. It is observed that the speedup gained over the sequential counterpart changed significantly depending on the structure of the portfolio. The portfolio is also applied to a training of neural networks which has been used for time series prediction. Result demonstrate that, portfolio is able to produce good prediction accuracy. |
| Author | Yao, Xin Akay, Rustu Basturk, Alper Kalinli, Adem |
| Author_xml | – sequence: 1 givenname: Rustu orcidid: 0000-0002-3585-3332 surname: Akay fullname: Akay, Rustu email: akay@erciyes.edu.tr organization: Department of Mechatronics Engineering, Erciyes University, Kayseri, Turkey – sequence: 2 givenname: Alper surname: Basturk fullname: Basturk, Alper organization: Department of Computer Engineering, Erciyes University, Kayseri, Turkey – sequence: 3 givenname: Adem surname: Kalinli fullname: Kalinli, Adem organization: Department of Computer Engineering, Erciyes University, Kayseri, Turkey – sequence: 4 givenname: Xin surname: Yao fullname: Yao, Xin organization: School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK |
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| CitedBy_id | crossref_primary_10_1016_j_knosys_2024_111628 crossref_primary_10_1016_j_patcog_2019_107104 crossref_primary_10_1016_j_swevo_2018_08_015 crossref_primary_10_1109_MCI_2020_2976182 crossref_primary_10_1007_s12293_022_00367_8 crossref_primary_10_1016_j_swevo_2018_08_005 crossref_primary_10_1016_j_swevo_2018_07_001 crossref_primary_10_1080_10556788_2018_1484123 crossref_primary_10_1016_j_swevo_2017_12_002 crossref_primary_10_1109_TEVC_2022_3209544 |
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