Adapting distributed evolutionary algorithms to heterogeneous hardware
Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distribut...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/114217 |
| Acceso en línea: | http://hdl.handle.net/11336/114217 |
| Access Level: | acceso abierto |
| Palabra clave: | distributed computing heterogeneity parallel algorithms metaheuristics https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distributed genetic algorithm to be customized for higher efficiency on any available hardware resources with different computing power, all of them collaborating to solve the same problem. We analyze the impact of heterogeneity in the resulting performance of a parallel metaheuristic and also its efficiency in time. Our conclusion is that the solution quality is comparable to that achieved by using a theoretically faster homogeneous platform, the traditional environment to execute this kind of algorithms, but an interesting finding is that those solutions are found with a lower numerical effort and even in lower running times in some cases. |
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