A hybrid fuzzy/genetic algorithm for the design of offshore oil production risers

This work presents the development and implementation of a synthesis and optimization procedure based on genetic algorithms (GA). A modified version of the basic GA, consisting in a hybrid fuzzy– GA, is considered. The performance of the optimization procedure is improved by employing linguistic knowledge and uncertainties inherent of human thinking. This eases the definition of the penalty and global objective functions that would be required in a standard GA procedure, which is not a simple task since it involves subjective assumptions. The application of the proposed procedure is focused on the design of steel catenary risers (SCRs) for floating oil production units at deep and ultra-deep waters. The application of this procedure is illustrated first by a small academic example, and then by a case study involving the design of lazy-wave configurations of SCRs