Practical genetic algorithms
How to solve it: modern heuristics
How to solve it: modern heuristics
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Genetic Algorithms
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Optimization with variable-fidelity models applied to wing design
Optimization with variable-fidelity models applied to wing design
A tutorial on support vector regression
Statistics and Computing
Design and Analysis of Experiments
Design and Analysis of Experiments
Tuning dynamic vibration absorbers by using ant colony optimization
Computers and Structures
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Finite Elements in Analysis and Design
Journal of Symbolic Computation
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In this work, a flat pressure bulkhead reinforced by an array of beams is designed using a suite of heuristic optimization methods (Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization and LifeCycle Optimization), and the Nelder-Mead simplex direct search method. The compromise between numerical performance and computational cost is addressed, calling for inexpensive, yet accurate analysis procedures. At this point, variable fidelity is proposed as a tradeoff solution. The difference between the low-fidelity and high-fidelity models at several points is used to fit a surrogate that corrects the low-fidelity model at other points. This allows faster linear analyses during the optimization; whilst a reduced set of expensive non-linear analyses are run "off-line," enhancing the linear results according to the physics of the structure. Numerical results report the success of the proposed methodology when applied to aircraft structural components. The main conclusions of the work are (i) the variable fidelity approach enabled the use of intensive computing heuristic optimization techniques; and (ii) this framework succeeded in exploring the design space, providing good initial designs for classical optimization techniques. The final design is obtained when validating the candidate solutions issued from both heuristic and classical optimization. Then, the best design can be chosen by direct comparison of the high-fidelity responses.