Geometric Firefly Algorithms on Graphical Processing Units

Husselmann, Alwyn V.; Hawick, K. A.

doi:10.1007/978-3-319-02141-6_12

Cited by 20 publications

(17 citation statements)

References 27 publications

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“…Because, in this case each firefly x i must estimate equation (13) O(n) times, for rest of fireflies x j , 1 ≤ j ≤ n and j = i [22]. Our distributed algorithm differ from this basic algorithm, maintaining an ordered tree structure of fireflies.…”

Section: Numerical Resultsmentioning

confidence: 99%

Firefly Inspired Improved Distributed Proximity Algorithm for D2D Communication

Pratap

Misra

2015

2015 IEEE International Parallel and Distributed Processing Symposium Workshop

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Device-to-Device (i.e. D2D) communication underlaying cellular technology not only increases system capacity but also utilizes the advantage of physical proximity of communicating devices to support services like proximity services, offload traffic from Base Station (i.e. BS) etc. But proximity discovery and synchronization among devices efficiently poses new research challenges for cellular networks. Inspired by the synchronization behaviour of firefly found in nature, the reported algorithms based on bio-inspired firefly heuristics for synchronization among devices as well as service interest among them having drawback of large convergence time and large message exchanges. Therefore, we propose an improved O(n log n) distributed firefly algorithm for D2D large scale networks using tree based topological mechanism using RSSI based ranging scheme.

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Section: Numerical Resultsmentioning

confidence: 99%

Firefly Inspired Improved Distributed Proximity Algorithm for D2D Communication

Pratap

Misra

2015

2015 IEEE International Parallel and Distributed Processing Symposium Workshop

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“…More precisely, Constraint (6) takes into account that total items shipped from each manufacturing plant to all the DCs in every period do not exceed the supply capacity of that manufacturing plant. Constraint (7) denotes the capacity storage of DCs and Constraint (8) indicates the retailers' storage capacity.…”

Section: • Objective Functionsmentioning

confidence: 99%

A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem

et al. 2018

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Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algorithm has been successfully applied to many engineering problems. However, this algorithm might be stuck in stagnation (the solutions do not enhance anymore) or possibly fall in premature convergence (fall into the local optimum) in searching space. It seems that both issues could be connected to exploitation and exploration. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this study, the classical firefly algorithm is modified such that make a balance between exploitation and exploration. The purposed modified algorithm ranks and sorts the initial solutions. Next, the operators named insertion, swap and reversion are utilized to search the neighbourhood of solutions in the second group, in which all these operators are chosen randomly. After that, the acquired solutions combined with the first group and the firefly algorithm finds the new potential solutions. A multi-echelon supply chain network problem is chosen to investigate the decisions associated with the distribution of multiple products that are delivered through multiple distribution centres and retailers and demonstrate the efficiency of the proposed algorithm.

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“…Existen pocas aproximaciones que utilicen algoritmo de luciérnaga sobre GPU [6,15]. Estos modelos han sido testeados sobre dominios continuos obteniendo buenos resultados con respecto a la ganancia de tiempo.…”

Section: Unidades De Procesamiento Gráficounclassified

“…Todos los óptimos locales, incluido el global pueden ser obtenidos simultáneamente [41][42][43][44][45]. Debido a la reciente aparición de este algoritmo existen pocos trabajos publicados sobre su implementación en GPU [6,15,40].…”

Section: Introductionunclassified

Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU

Vidal

Olivera

2018

DYNA

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The Deoxyribonucleic Acid Fragment Assembly Problem (DNA-FAP) consists in reconstruct a DNA chain from a set of fragments taken randomly. Several authors solved the DNA-FAP using different approaches. In general, although it was obtaining good results; the computational time associated is high. The Firefly Algorithm (FA) is a bioinspired model based on the behaviour of fireflies. Considering that FA is a population bioinspired algorithm is possible design a parallel model of itself on Graphics Processing. In this work, a FA especially development for its execution on GPU is presented in order to accelerate the computational process to solve the DNA-FAP. Through several experiments the efficiency of the algorithm and the quality of the results were demonstrated.Keywords: fragment assembly problem; firefly algorithm; graphics processing units; optimization; parallelism. Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU ResumenEl problema de ensamblado de fragmentos de cadenas de ácido desoxirribonucleico (Deoxyribonucleic Acid Fragment Assembly Problem, DNA-FAP) consiste en la reconstrucción de cadenas de ADN desde un conjunto de fragmentos tomados aleatoriamente. El DNA-FAP ha sido resuelto por diferentes autores utilizando distintos enfoques. Aunque se obtienen buenos resultados, el tiempo computacional asociado es alto. El algoritmo de luciérnaga (Firefly Algorithm, FA) es un modelo bioinspirado basado en el comportamiento de las luciérnagas. Al ser un algoritmo bioinspirado poblacional es posible generar un modelo paralelo del mismo sobre Unidades de Procesamiento Gráfico (Graphics Processing Units, GPU). En este trabajo un algoritmo de luciérnaga es diseñado especialmente para ser ejecutado sobre una arquitectura GPU de manera tal de acelerar el proceso computacional buscando resolver el DNA-FAP. A través de diferentes experimentos se demuestra la eficiencia computacional y la calidad de los resultados obtenidos.Palabras clave: ensamblado de fragmentos de ADN; algoritmo de luciérnaga; unidades de procesamiento gráfico; optimización; paralelismo. IntroducciónLa necesidad de conocer y predecir mutaciones somáticas y realizar distintos estudios relacionados con el desarrollo de los seres vivos es un tópico de relevancia por su impacto en la medicina para detección y tratamiento de patologías, la investigación forense y el desarrollo de medicamentos. Entre los problemas relacionados con el genoma podemos encontrar el estudio del ensamblado de cadenas de ADN (Deoxyribonucleic Acid Fragment Assembly Problem, DNA-FAP), donde dado un conjunto de cientos o miles de How to cite: Vidal, P.J. and Olivera, A.C., Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU. DYNA, 85(204), pp. 108-116, March, 2018. fragmentos de ADN, que pueden contener errores, debemos encontrar la secuencia de ADN original a partir de las permutaciones de los fragmentos que mejor representen a dicha secuencia [17].Existen herramientas que automatizan el secuenciamien...

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Geometric Firefly Algorithms on Graphical Processing Units

Cited by 20 publications

References 27 publications

Firefly Inspired Improved Distributed Proximity Algorithm for D2D Communication

Firefly Inspired Improved Distributed Proximity Algorithm for D2D Communication

A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem

Ensamblado de fragmentos de ADN utilizando un novedoso algoritmo de luciérnaga en GPU

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