A COMPARATIVE STUDY APPLIED TO RISERS OPTIMIZATION USING BIO-INSPIRED ALGORITHMS

This work presents a comparative study of three different bio-inspired optimization methodologies applied to the optimization of a Steel Catenary Riser (SCR) for floating oil production systems. This problem arises from oil production activities that reach deep and ultra-deep waters. The optimization of such a system requires a time-consuming objective function evaluation, for this reason, we adopted a simplified evaluation method that uses a catenary analytic formulation. Finally the effectiveness of the employed algorithms, Artificial Immune System (AIS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), is measured by the number of the objective function calculations and the respective values achieved. Results indicate that AIS approach is more effective than GA and PSO, generating better solutions with a small number of evaluations.

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