TY - GEN
T1 - Using computational intelligence and parallelism to solve an industrial design problem
AU - Asteasuain, Fernando
AU - Carballido, Jessica A.
AU - Vazquez, Gustavo E.
AU - Ponzoni, Ignacio
PY - 2006
Y1 - 2006
N2 - In this work we present a critical analysis of three novel parallel-distributed implementations of a multi-objective genetic algorithm (pdGAs) for instrumentation design applications. The pdGAs aim at establishing a sensible configuration of sensors for the initialization of instrumentation design studies of industrial processes. They were built on the basis of an evolutionary island model, the master-worker paradigm, and different migration and parameter control policies. The performance of the resulting implementations was assessed by testing algorithmic behavior on an industrial example that corresponds to an ammonia synthesis plant. The three pdGAs' results were highly satisfactory in terms of speed-up, efficiency and instrumentation quality, thus revealing to constitute competitive tools with strong potential for their use in the industrial area. As well, from an overall point of view, the pdGA version with adaptive parameter control represents the best implementation's alternative.
AB - In this work we present a critical analysis of three novel parallel-distributed implementations of a multi-objective genetic algorithm (pdGAs) for instrumentation design applications. The pdGAs aim at establishing a sensible configuration of sensors for the initialization of instrumentation design studies of industrial processes. They were built on the basis of an evolutionary island model, the master-worker paradigm, and different migration and parameter control policies. The performance of the resulting implementations was assessed by testing algorithmic behavior on an industrial example that corresponds to an ammonia synthesis plant. The three pdGAs' results were highly satisfactory in terms of speed-up, efficiency and instrumentation quality, thus revealing to constitute competitive tools with strong potential for their use in the industrial area. As well, from an overall point of view, the pdGA version with adaptive parameter control represents the best implementation's alternative.
KW - Distributed computing
KW - Genetic algorithms
KW - Instrumentation design
UR - http://www.scopus.com/inward/record.url?scp=33751399776&partnerID=8YFLogxK
U2 - 10.1007/11874850_23
DO - 10.1007/11874850_23
M3 - Contribución a la conferencia
AN - SCOPUS:33751399776
SN - 3540454624
SN - 9783540454625
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 188
EP - 197
BT - Advances in Artificial Intelligence - IBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium, Proceedings
PB - Springer Verlag
T2 - IBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium
Y2 - 23 October 2006 through 27 October 2006
ER -