A New Hybrid Gravitational Search- Black Hole Algorithm
This paper studies the solution of optimization problems using a new hybrid population-based algorithm. The Gravitational Search – Black Hole Algorithm (GSBHA) is proposed as a combination of the Black Hole Algorithm (BHA) and Gravitational Search Algorithm (GSA). The main idea is to improve the st...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
Universiti Malaysia Pahang
2016
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| 主題: | |
| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/15750/1/P113%20pg834-842.pdf http://umpir.ump.edu.my/id/eprint/15750/ http://ee.ump.edu.my/ncon/wp-content/uploads/2016/10/Proceeding-NCON-PGR-2016.zip |
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| 要約: | This paper studies the solution of optimization problems using a new hybrid population-based algorithm. The
Gravitational Search – Black Hole Algorithm (GSBHA) is proposed as a combination of the Black Hole Algorithm (BHA) and Gravitational Search Algorithm (GSA). The main idea is to improve the standard BHA by using GSA. To evaluate the performance of GSBHA, standard test functions of CEC 2014 for real-parameters are used to compare the hybrid algorithm with both the standard BHA and GSA algorithms in evolving the best solution. The results obtained demonstrate better performance of the hybrid algorithm and better capability to escape from local optimums with faster convergence than the
standard BHA and GSA. |
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