Solving the DNA fragment assembly problem with a parallel discrete firefly algorithm implemented on GPU

Vidal, Pablo Javier; Olivera, Ana Carolina

doi:10.2298/csis170510009v

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…For instance, Hughes et al (2016) present a collection of GA variations: recentering-restarting GA, island model GA, and a GA that employs ring species. An interesting contribution is made by Vidal and Olivera (2018). This work develops a discrete firefly algorithm (FA) on a graphics processing unit (GPU) architecture aiming to speed up the search process for solving the FAP.…”

Section: The Fragment Assembly Problemmentioning

confidence: 99%

On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

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Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

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Section: The Fragment Assembly Problemmentioning

confidence: 99%

On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

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“…Initialize the ecosystem (no) as in (15) Evaluate the multiple fitness values of each organism by using the ( 12) and ( 13)…”

Section: Fast Non-dominated Sorting Multi-objective Symbiotic Organisms Search Algorithm (Fnsmosos)mentioning

confidence: 99%

“…Evolutionary computation (EC) techniques for solving multi-objective problems are more useful since they are able to generate a set of multiple solutions in a single optimization run. For different kinds of optimization problems, there are a lot of different EC techniques [13][14][15][16]. Among these techniques, symbiotic organism search (SOS) algorithm which simulates the symbiotic interaction strategies between organisms in an ecosystem has increasingly become popularity in recent years because it presents more robust results with a faster convergence speed for optimization problems in various domains [17].…”

Section: Introductionmentioning

confidence: 99%

A fast non-dominated sorting multi-objective symbiotic organism search algorithm for energy efficient locomotion of snake robot

Baysal

Altaş

2022

ComSIS

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This paper deals with energy efficient locomotion of a wheel-less snake robot. This is very crucial for potential applications of untethered snake robots. The optimum gait parameters for the energy efficient locomotion of the snake robot are obtained with two different multi-objective algorithms based on symbiotic organism search algorithm by considering both minimizing the average power consumption and maximizing the forward velocity of the robot. This paper also investigates the energy efficient locomotion of the snake robot under different environment conditions. The obtained results demonstrate that both proposed methods achieve satisfying stable results regarding power consumption reduction with optimal forward velocity for lateral undulation motion. However, it is seen that fast non-dominated sorting multi-objective symbiotic organism search algorithm provides advantage on obtaining a uniformly distributed solution set with a good diversity only in a single run. This paper is important in terms of presenting useful results for developing efficient motion and environmental adaptability of the snake robot.

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“…Since then various algorithms [13] have been proposed in the literature to solve this problem. Ant Colony Optimization [14,15,16], Genetic Algorithm [17,18,10], Artificial Bee Colony [19], Hierarchy Clustering, Simulated Annealing, Firefly Algorithm [20], Tabu Search [11], De Novo Assembly Algorithm [21] and Crow Search Algorithm [12] techniques have been leveraged too.…”

Section: Consensus Stagementioning

confidence: 99%

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Jain¹,

Bharti²

2021

Preprint

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With the advent of Genome Sequencing, the field of Personalized Medicine has been revolutionized. From drug testing and studying diseases and mutations to clan genomics, studying the genome is required. However, genome sequence assembly is a very complex combinatorial optimization problem of computational biology. PSO is a popular metaheuristic swarm intelligence optimization algorithm, used to solve combinatorial optimization problems. In this paper, we propose a new variant of PSO to address this permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of the PSO proposed in the literature. The analysis is conducted on four DNA coverage datasets. The conducted analysis demonstrates that the proposed model attain a better performance with better reliability and consistency in comparison to others competitive methods in all cases.

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Solving the DNA fragment assembly problem with a parallel discrete firefly algorithm implemented on GPU

Cited by 7 publications

References 28 publications

On the role of metaheuristic optimization in bioinformatics

On the role of metaheuristic optimization in bioinformatics

A fast non-dominated sorting multi-objective symbiotic organism search algorithm for energy efficient locomotion of snake robot

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

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