Comparison of swarm intelligence algorithms for high dimensional optimization problems
High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selectin...
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
Institute of Advanced Engineering and Science
2018
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| 主題: | |
| オンライン･アクセス: | http://irep.iium.edu.my/65214/1/65214_Comparison%20of%20swarm%20intelligence%20algorithms%20for%20high%20dimensional%20optimization%20problems.pdf http://irep.iium.edu.my/65214/2/65214_Comparison%20of%20swarm%20intelligence%20algorithms%20for%20high%20dimensional%20optimization%20problems_SCOPUS.pdf http://irep.iium.edu.my/65214/ http://www.iaescore.com/journals/index.php/IJEECS/article/view/10622/8786 |
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| 要約: | High dimensional optimization considers being one of the most challenges
that face the algorithms for finding an optimal solution for real-world
problems. These problems have been appeared in diverse practical fields
including business and industries. Within a huge number of algorithms,
selecting one algorithm among others for solving the high dimensional
optimization problem is not an easily accomplished task. This paper presents
a comprehensive study of two swarm intelligence based algorithms: 1-
particle swarm optimization (PSO), 2-cuckoo search (CS).The two
algorithms are analyzed and compared for problems consisting of high
dimensions in respect of solution accuracy, and runtime performance by
various classes of benchmark functions. |
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