Enhanced stochastic fractal search algorithm with chaos

Citation

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).

Abstract

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.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1109/ICSGRC.2016.7813295
Publisher:	IEEE
Keywords:	Benchmark functions; Chaotic fractal search; Optimization algorithm
Depositing User:	Nabilah Mustapa
Date Deposited:	07 Jun 2017 08:34
Last Modified:	07 Jun 2017 08:34
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICSGRC.2016.7813295
URI:	http://psasir.upm.edu.my/id/eprint/55683
Statistic Details:	View Download Statistic

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