ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment

•Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.•The hybridization is based on low level co-evolutionary hybrid scheme.•The proposed scheme produced better performance on mathematical benchmark functions.•Finding longest consecutive substring problem was used as testing...

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Veröffentlicht in:	Expert systems with applications Jg. 99; S. 56 - 70
Hauptverfasser:	Issa, Mohamed, Hassanien, Aboul Ella, Oliva, Diego, Helmi, Ahmed, Ziedan, Ibrahim, Alzohairy, Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.06.2018 Elsevier BV
Schlagworte:	Adaptive algorithms Algorithms Alignment Biological effects Computing time Heuristic Heuristic methods Longest consecutive substrings Meta-heuristics algorithms Operators Pairwise local alignment Parameters Particle swarm optimization Particle swarm optimization (PSO) Route optimization Sine cosine algorithm (SCA) Smith-Waterman alignment algorithm Trigonometric functions Sine cosine algorithm (SCA) Particle swarm optimization (PSO) Smith-Waterman alignment algorithm Longest consecutive substrings Meta-heuristics algorithms Pairwise local alignment
ISSN:	0957-4174, 1873-6793
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Abstract	•Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.•The hybridization is based on low level co-evolutionary hybrid scheme.•The proposed scheme produced better performance on mathematical benchmark functions.•Finding longest consecutive substring problem was used as testing case study. The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.
AbstractList	The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time. •Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.•The hybridization is based on low level co-evolutionary hybrid scheme.•The proposed scheme produced better performance on mathematical benchmark functions.•Finding longest consecutive substring problem was used as testing case study. The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.
Author	Issa, Mohamed Alzohairy, Ahmed Ziedan, Ibrahim Oliva, Diego Helmi, Ahmed Hassanien, Aboul Ella
Author_xml	– sequence: 1 givenname: Mohamed surname: Issa fullname: Issa, Mohamed organization: Computer Engineering and Systems Department, Faculty of Engineering, Zagazig University, Egypt – sequence: 2 givenname: Aboul Ella orcidid: 0000-0002-9989-6681 surname: Hassanien fullname: Hassanien, Aboul Ella email: aboitcairo@fci-cu.edu.eg organization: Faculty of Computers and Information, Cairo University, Egypt – sequence: 3 givenname: Diego surname: Oliva fullname: Oliva, Diego email: doliva@ucm.es organization: Departamento de Ciencias Computacionales, Universidad de Guadalajara, Mexico – sequence: 4 givenname: Ahmed surname: Helmi fullname: Helmi, Ahmed organization: Computer Engineering and Systems Department, Faculty of Engineering, Zagazig University, Egypt – sequence: 5 givenname: Ibrahim surname: Ziedan fullname: Ziedan, Ibrahim organization: Computer Engineering and Systems Department, Faculty of Engineering, Zagazig University, Egypt – sequence: 6 givenname: Ahmed surname: Alzohairy fullname: Alzohairy, Ahmed organization: Genetic Department, Faculty of Agriculture, Zagazig University, Egypt
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Keywords	Sine cosine algorithm (SCA) Particle swarm optimization (PSO) Smith-Waterman alignment algorithm Longest consecutive substrings Meta-heuristics algorithms Pairwise local alignment
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Snippet	•Sine cosine algorithm is enhanced by hybridizing particle swarm optimization.•The hybridization is based on low level co-evolutionary hybrid scheme.•The... The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as...
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SubjectTerms	Adaptive algorithms Algorithms Alignment Biological effects Computing time Heuristic Heuristic methods Longest consecutive substrings Meta-heuristics algorithms Operators Pairwise local alignment Parameters Particle swarm optimization Particle swarm optimization (PSO) Route optimization Sine cosine algorithm (SCA) Smith-Waterman alignment algorithm Trigonometric functions
Title	ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment
URI	https://dx.doi.org/10.1016/j.eswa.2018.01.019 https://www.proquest.com/docview/2041745848
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