Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation

This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radiu...

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Main Authors:	Ahmad Nor Kasruddin, Nasir, Tokhi, M. O.
Format:	Article
Language:	English
Published:	Elsevier 2015
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/1/Novel%20Metaheuristic%20Hybrid%20Spiral-Dynamic%20Bacteria-Chemotaxis%20Algorithms%20for%20Global%20Optimisation.pdf

id	ump-7794
recordtype	eprints
spelling	ump-77942018-08-21T03:37:20Z http://umpir.ump.edu.my/id/eprint/7794/ Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation Ahmad Nor Kasruddin, Nasir Tokhi, M. O. Q Science (General) This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence result- ing in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria move- ment affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while defining a small step-size gives high accuracy but produces slower con- vergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as para- metric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator. Elsevier 2015-12-04 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7794/1/Novel%20Metaheuristic%20Hybrid%20Spiral-Dynamic%20Bacteria-Chemotaxis%20Algorithms%20for%20Global%20Optimisation.pdf Ahmad Nor Kasruddin, Nasir and Tokhi, M. O. (2015) Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation. Applied Soft Computing, 27. pp. 357-375. ISSN 1568-4946 (print); 1872-9681 (online) http://dx.doi.org/10.1016/j.asoc.2014.11.030 doi:10.1016/j.asoc.2014.11.030
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Ahmad Nor Kasruddin, Nasir Tokhi, M. O. Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
description	This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence result- ing in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria move- ment affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while defining a small step-size gives high accuracy but produces slower con- vergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as para- metric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator.
format	Article
author	Ahmad Nor Kasruddin, Nasir Tokhi, M. O.
author_facet	Ahmad Nor Kasruddin, Nasir Tokhi, M. O.
author_sort	Ahmad Nor Kasruddin, Nasir
title	Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
title_short	Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
title_full	Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
title_fullStr	Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
title_full_unstemmed	Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation
title_sort	novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation
publisher	Elsevier
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/ http://umpir.ump.edu.my/id/eprint/7794/1/Novel%20Metaheuristic%20Hybrid%20Spiral-Dynamic%20Bacteria-Chemotaxis%20Algorithms%20for%20Global%20Optimisation.pdf
first_indexed	2023-09-18T22:04:47Z
last_indexed	2023-09-18T22:04:47Z
_version_	1777414635795775488

Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation

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