Bio-Inspired Algorithms For The Optimization Of Offshore Oil Production Systems

This work describes the development, implementation, and assessment of enhanced variants of three different groups of bio-inspired methodologies: genetic algorithms, particle swarm optimization, and artificial immune system. The algorithms are implemented on a computational tool for the synthesis and optimization of offshore oil production risers that connect a floating platform at the sea surface to the wellheads at the sea bottom. Optimization procedures using bio-inspired algorithms for such real-world engineering problems require the calculation of the objective function through a large number of time-consuming finite element nonlinear dynamic analyses, for the evaluation of the structural behavior of each candidate configuration. Therefore, the performance of the algorithms may be measured by the smaller number of objective function evaluations associated to a given target fitness value. The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions. Copyright (c) 2012 John Wiley & Sons, Ltd.