Using Computational Intelligence and Parallelism to Solve an Industrial Design Problem


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.