A Self-Adaptive Fireworks Algorithm for Classification Problems
Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8419712/ |
| _version_ | 1818924439568384000 |
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| author | Yu Xue Binping Zhao Tinghuai Ma Wei Pang |
| author_facet | Yu Xue Binping Zhao Tinghuai Ma Wei Pang |
| author_sort | Yu Xue |
| collection | DOAJ |
| description | Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this paper, a self-adaptive FWA (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization, and differential evolution on most of the training sets and test sets. |
| first_indexed | 2024-12-20T02:25:21Z |
| format | Article |
| id | doaj.art-16ff08f3b6e64a82a749abedb25200b8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:25:21Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-16ff08f3b6e64a82a749abedb25200b82022-12-21T19:56:43ZengIEEEIEEE Access2169-35362018-01-016444064441610.1109/ACCESS.2018.28584418419712A Self-Adaptive Fireworks Algorithm for Classification ProblemsYu Xue0https://orcid.org/0000-0002-9069-7547Binping Zhao1Tinghuai Ma2https://orcid.org/0000-0003-2320-1692Wei Pang3School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Natural and Computing Sciences, University of Aberdeen, Aberdeen, U.K.Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this paper, a self-adaptive FWA (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization, and differential evolution on most of the training sets and test sets.https://ieeexplore.ieee.org/document/8419712/Classificationevolutionary classification algorithmfireworks algorithm (FWA)self-adaptiveoptimization |
| spellingShingle | Yu Xue Binping Zhao Tinghuai Ma Wei Pang A Self-Adaptive Fireworks Algorithm for Classification Problems IEEE Access Classification evolutionary classification algorithm fireworks algorithm (FWA) self-adaptive optimization |
| title | A Self-Adaptive Fireworks Algorithm for Classification Problems |
| title_full | A Self-Adaptive Fireworks Algorithm for Classification Problems |
| title_fullStr | A Self-Adaptive Fireworks Algorithm for Classification Problems |
| title_full_unstemmed | A Self-Adaptive Fireworks Algorithm for Classification Problems |
| title_short | A Self-Adaptive Fireworks Algorithm for Classification Problems |
| title_sort | self adaptive fireworks algorithm for classification problems |
| topic | Classification evolutionary classification algorithm fireworks algorithm (FWA) self-adaptive optimization |
| url | https://ieeexplore.ieee.org/document/8419712/ |
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