Parallelization of Enhanced Firework Algorithm using MapReduce

Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algo...
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Autor*in:	Ludwig, Simone A. [verfasserIn] Dawar, Deepak [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Umfang:	1 Online-Ressource

Übergeordnetes Werk:	Enthalten in: International journal of swarm intelligence research - Hershey, Pa : IGI Global, 2010, 6(2015), 2, Seite 32-51
Übergeordnetes Werk:	volume:6 ; year:2015 ; number:2 ; pages:32-51

Links:	Link aufrufen Abstract

DOI / URN:	10.4018/IJSIR.2015040102

Katalog-ID:	NLEJ251831795

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title_auth	Parallelization of Enhanced Firework Algorithm using MapReduce
abstract	Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions
abstractGer	Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions
abstract_unstemmed	Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions
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