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This paper presents BIOS (acronym for Biologically Inspired Optimization System), an object-oriented framework written in C++, aimed at heuristic optimization with a focus on Surrogate-Based Optimization (SBO) and structural problems. The use of SBO to deal with structural optimization has grown considerably in recent years due to the outstanding gain in efficiency, often with little loss in accuracy. This is especially promising when adaptive sampling techniques are used. However, many issues are yet to be addressed before SBO can be employed reliably in most optimization problems. In that sense, continuous experimentation, testing and comparison are needed, which can be more easily carried out in an existing framework. The architecture is designed to implement conventional nature inspired algorithms and Sequential Approximated Optimization (SAO). The system aims to be efficient, easy to use and extensible. The efficiency and accuracy of the system are assessed on a set of benchmarks, and on the optimization of functionally graded structures. Excellent results are obtained.

This work presents an efficient methodology for the optimum design of functionally graded structures using a Krigingbased approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process. In this approach, the surrogate model is used to replace the high-fidelity structural responses obtained by a NURBS-based isogeometric analysis. In addition, the impact of key factors on surrogate modelling, as the correlation function, the infill criterion used to update the surrogate model, and the constraint handling is assessed for accuracy, efficiency, and robustness. The design variables are related to the volume fraction distribution and the thickness. Displacement, fundamental frequency, buckling load, mass, and ceramic volume fraction are used as objective functions or constraints. The effectiveness and accuracy of the proposed algorithm are illustrated through a set of numerical examples. Results show a significant reduction in the computational effort over the conventional approach.

Use of fiber-reinforced laminated composites has proved itself as a valuable option in the manufacturing of risers, particularly for deepwater applications, a scenario where its lightweight related properties and good fatigue resistance are most needed. In addition, its use allows these structures to be tailored to meet specific manufacturing, safety, and stability criteria. This paper proposes an optimization model to composite risers in a free-hanging catenary configuration that considers multiple load cases and two objective functions. The optimization is carried out using a modified version of the Nondominated Sorting Genetic Algorithm II (NSGA-II). The riser structural analysis is performed by an inextensible cable model that accounts for the vertical static loads, floater offset and current loads in a fast and efficient way. The proposed algorithm is validated using a benchmark problem and applied to obtain the Pareto Front of a composite riser.