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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書誌詳細
主要な著者: Bashath, Samar, Ismail, Amelia Ritahani
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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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.