Enhanced stochastic fractal search algorithm with chaos
This study presents modifications to a metaheuristic algorithm inspired by natural phenomenon of growth with its performance assessment in comparison to its original predecessor algorithm on various standard classical benchmark functions. The modified algorithm aims to improve the Stochastic Fractal...
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2016
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my.upm.eprints.556832017-06-07T08:34:34Z http://psasir.upm.edu.my/id/eprint/55683/ Enhanced stochastic fractal search algorithm with chaos Tuan Abdul Rahman, Tuan Ahmad Zahidi Tokhi, Mohammad Osman This study presents modifications to a metaheuristic algorithm inspired by natural phenomenon of growth with its performance assessment in comparison to its original predecessor algorithm on various standard classical benchmark functions. The modified algorithm aims to improve the Stochastic Fractal Search (SFS) algorithm in terms of convergence speed and fitness accuracy. The performance of SFS is affected by a constant β that is used to decrease the size of Gaussian jumps and then encourage a more localized search for individuals. Five different chaotic maps have been selected in this study. The influence of these chaotic maps on convergence rate and solution accuracy is investigated using several classical standard benchmark functions. Overall results show that SFS algorithm with Gauss/Mouse map results in significant improvement in comparison to its original version. IEEE 2016 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/55683/1/Enhanced%20stochastic%20fractal%20search%20algorithm%20with%20chaos.pdf Tuan Abdul Rahman, Tuan Ahmad Zahidi and Tokhi, Mohammad Osman (2016) Enhanced stochastic fractal search algorithm with chaos. In: 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC 2016), 8 Aug. 2016, UiTM Shah Alam, Selangor, Malaysia. (pp. 22-27). 10.1109/ICSGRC.2016.7813295 |
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This study presents modifications to a metaheuristic algorithm inspired by natural phenomenon of growth with its performance assessment in comparison to its original predecessor algorithm on various standard classical benchmark functions. The modified algorithm aims to improve the Stochastic Fractal Search (SFS) algorithm in terms of convergence speed and fitness accuracy. The performance of SFS is affected by a constant β that is used to decrease the size of Gaussian jumps and then encourage a more localized search for individuals. Five different chaotic maps have been selected in this study. The influence of these chaotic maps on convergence rate and solution accuracy is investigated using several classical standard benchmark functions. Overall results show that SFS algorithm with Gauss/Mouse map results in significant improvement in comparison to its original version. |
format |
Conference or Workshop Item |
author |
Tuan Abdul Rahman, Tuan Ahmad Zahidi Tokhi, Mohammad Osman |
spellingShingle |
Tuan Abdul Rahman, Tuan Ahmad Zahidi Tokhi, Mohammad Osman Enhanced stochastic fractal search algorithm with chaos |
author_facet |
Tuan Abdul Rahman, Tuan Ahmad Zahidi Tokhi, Mohammad Osman |
author_sort |
Tuan Abdul Rahman, Tuan Ahmad Zahidi |
title |
Enhanced stochastic fractal search algorithm with chaos |
title_short |
Enhanced stochastic fractal search algorithm with chaos |
title_full |
Enhanced stochastic fractal search algorithm with chaos |
title_fullStr |
Enhanced stochastic fractal search algorithm with chaos |
title_full_unstemmed |
Enhanced stochastic fractal search algorithm with chaos |
title_sort |
enhanced stochastic fractal search algorithm with chaos |
publisher |
IEEE |
publishDate |
2016 |
url |
http://psasir.upm.edu.my/id/eprint/55683/1/Enhanced%20stochastic%20fractal%20search%20algorithm%20with%20chaos.pdf http://psasir.upm.edu.my/id/eprint/55683/ |
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