PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
ModGen: a model generator for instrumentation analysis
Advances in Engineering Software
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Data Processing and Reconciliation for Chemical Process Operations
Data Processing and Reconciliation for Chemical Process Operations
A novel application of evolutionary computing in process systems engineering
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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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.