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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Zusammenfassung:•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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.01.019