Parallel Ant Colony Optimization for the HP Protein Folding Problem

Llanes, Antonio; Vélez, C.A.; Sánchez, Antonia; Pérez‐Sánchez, Horacio; Cecilia, José M.

doi:10.1007/978-3-319-31744-1_54

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Many researchers have tried exploiting latest GPU architectures in order to speed optimize the convergence of ACO. ACO GPU implementation expands to many fields, such as edge detection ( [25] [38]), protein folding [20], solving Multidimensional Knapsack Problems (MKPs) [21] and Vertex coloring problems [39]. Moreover, researchers have used GPU implementations of ACO for classification ( [40] [41]) and scheduling ( [42] [43]) with various speedups compared to the sequential execution.…”

Section: Gpusmentioning

confidence: 99%

Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions

Dzalbs

Kalganova

2020

Computers & Industrial Engineering

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Section: Gpusmentioning

confidence: 99%

Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions

Dzalbs

Kalganova

2020

Computers & Industrial Engineering

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“…Approximation algorithms are efficient algorithms that find approximate solutions to NP-hard optimization problems with provable guarantees about the distance of the returned solution to the optimal one. Many evolutionary algorithms have been proposed to tackle the HP folding problem [29], for example, genetic algorithms in combination with different mechanisms [5,46], ant colony optimization [23,28], differential evolution [29], and thermal cycling [19]. Approximation algorithms, on the other hand, provide a guaranteed ratio of the number of 1-1 contacts to the upper bound.…”

Section: Protein Folding Approximationmentioning

confidence: 99%

Application of local rules and cellular automata in representing protein translation and enhancing protein folding approximation

2018

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It is self-evident that the coarse-grained view of transcription and protein translation is a result of certain computations. Although there is no single definition of the term "computation," protein translation can be implemented over mathematical models of computers. Protein folding, however, is a combinatorial problem; it is still unknown whether a fast, accurate, and optimal folding algorithm exists. The discovery of near-optimal folds depends on approximation algorithms and heuristic searches. The hydrophobic-hydrophilic (HP) model is a simplified representation of some of the realities of protein structure. Despite the simplified representation, the folding problem in the HP model was proven to be NP-complete. We use simple and local rules to model translation and folding of proteins. Local rules imply that at a certain level of abstraction an entity can move from a state to another based on its state and information collected from its neighborhood. Also, the rules are simple in a sense that they do not require complicated computation. We use one-dimensional cellular automata to describe translation of mRNA into protein. Cellular automata are discrete models of computation that use local interactions to produce a global behavior of some sort. We will also discuss how local rules can improve approximation algorithms of protein folding and give an example of a CA that accept a certain family of strings to achieve half H-H contacts.

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“…Portanto, o emprego de algoritmos de otimização para solucionar o problema PSP apresenta-se como uma boa alternativa ante aos métodos tradicionais. Podem-se citar diversas técnicas de otimização computacional aplicadas ao PSP, como Algoritmos Evolutivos [1,2,6,15,20,23,24], Otimização por Colônia de Formiga [10,14,18,21,25], Otimização por Enxame de Abelhas [3,19], entre outros [26,27]. Vale ressaltar que, em geral, usando esses métodos para problemas extremamente complexos não é obtida uma única solução (mas um conjunto de soluções); inclusive a solução ótima pode nunca ser alcançada, e nestes casos, pode ser difícil definir qual a melhor solução no conjunto das possíveis soluções.…”

Section: Introductionunclassified

Comparando algoritmos de otimização computacional aplicados ao problema de predição de estruturas proteicas com modelo HP-2D

Brasil

Dias

2017

RBCA

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Os métodos de otimização computacional são largamente aplicados a diversos tipos de problemas complexos a fim de encontrar soluções para os mesmos. Neste trabalho, os métodos de otimização estudados foram o Algoritmo Evolutivo (AE) e a Otimização por Colônia de Formiga (ACO – Ant Colony Optimization). Ambos são bioinspirados, isto é, são baseados em processos que ocorrem na natureza. Neste caso específico, o AE e o ACO foram utilizados para encontrar soluções ao desafiador problema de predição de proteínas (PSP – Protein Structure Problem), caracterizado como um problema não polinomial. Foi realizada uma comparação entre estes dois métodos aplicados ao PSP usando modelo HP-2D com algumas sequências específicas, tanto do ponto de vista computacional quanto bioquímico. Os resultados mostraram que o ACO é melhor em termo de energia, enquanto que o AE é mais adequado em termo de tempo, especialmente para proteínas maiores.

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Parallel Ant Colony Optimization for the HP Protein Folding Problem

Cited by 5 publications

References 23 publications

Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions

Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions

Application of local rules and cellular automata in representing protein translation and enhancing protein folding approximation

Comparando algoritmos de otimização computacional aplicados ao problema de predição de estruturas proteicas com modelo HP-2D

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