Enhanced stochastic fractal search algorithm with chaos
| Autor: | Tuan A. Z. Rahman, M. Osman Tokhi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Gaussian Chaotic 02 engineering and technology 01 natural sciences 010305 fluids & plasmas symbols.namesake Fractal Rate of convergence Search algorithm 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Benchmark (computing) symbols 020201 artificial intelligence & image processing Algorithm design Algorithm Metaheuristic Mathematics |
| Zdroj: | 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC). |
| DOI: | 10.1109/icsgrc.2016.7813295 |
| Popis: | 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 p 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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