A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

Huang, Ko-Wei; Chen, Jui-Le; Yang, Chu-Sing; Tsai, Chun-Wei

doi:10.1007/s00521-014-1659-0

Cited by 33 publications

(15 citation statements)

References 64 publications

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“…Finally, we should note that we did not compare PALS2-many with the algorithm proposed in Huang et al (2015). The main reason is because the goals of the works are different; our paper is focused on obtaining the minimum number of contigs while that paper is focused on the overlap among the fragments.…”

Section: Comparison Against Other Existing Pals Variants and Other Asmentioning

confidence: 93%

“…The most popular heuristic algorithm in the literature is called the Problem Aware Local Search (PALS) (Alba and Luque 2007), which is very good at finding accurate solutions and much faster than previously existing techniques. Commonly used metaheuristics include genetic algorithms (GAs) (Parsons et al 1995;Li and Khuri 2004;Luque et al 2006;Nebro et al 2008;Minetti et al 2008), simulated annealing (SA) (Burks et al 1994), ant colony optimization (ACO) (Meksangsouy and Chaiyaratana 2003), iterative optimization (Kubalik et al 2010), artificial bee colony (ABC) algorithm (Firoz et al 2012), and particle swarm optimization (PSO) (Huang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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An improved problem aware local search algorithm for the DNA fragment assembly problem

et al. 2015

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DNA fragment assembly is a critical and essential early task in a genome project. This task leads to an NP-hard combinatorial optimization problem, and thus, efficient approximate algorithms are required to tackle large problem instances. The Problem Aware Local Search (PALS) is one of the most efficient heuristics for this problem in the literature. PALS gives fairly good solutions but the probability of premature convergence to local optima is significant. In this paper, we propose two modifications to the PALS heuristic in order to ameliorate its performance. The first modification enables the algorithm to improve the tentative solutions in a more appropriate and beneficial way. The second modification permits a significant reduction in the computational demands of the algorithm without significant accuracy loss. Computational experiments confirm that our proposals lead to a more efficient and robust assembler, improving both accuracy and efficiency.

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Section: Comparison Against Other Existing Pals Variants and Other Asmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

An improved problem aware local search algorithm for the DNA fragment assembly problem

et al. 2015

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“…Therefore, the goal of genome projects is to reconstruct the original genome sequence of an organism. To achieve the goal, DNA fragment assembly process is divided into three phases [7,10] 1. Overlap Phase is tasked to find the common sequence among the prefix of one sequence and suffix of another.…”

Section: Understanding Dna Assemblymentioning

confidence: 99%

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

al-Rifaie

2016

Studies in Computational Intelligence

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This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS) -a swarm intelligence algorithm -to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to overlap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments realworld data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper.

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Section: Understanding Dna Assemblymentioning

confidence: 99%

Investigating stochastic diffusion search in DNA sequence assembly problem

al-Rifaie

2015

2015 SAI Intelligent Systems Conference (IntelliSys)

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Abstract-This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS) -swarm intelligence algorithm -to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally the performance of the algorithm is compared against several other techniques, demonstrating its weaknesses and strength in the experiments presented in the paper.

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A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

Cited by 33 publications

References 64 publications

An improved problem aware local search algorithm for the DNA fragment assembly problem

An improved problem aware local search algorithm for the DNA fragment assembly problem

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

Investigating stochastic diffusion search in DNA sequence assembly problem

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